Given the continuous expansion of training datasets and the rapid evolution of deep learning architectures， vision foundation models and large language models have demonstrated remarkable generalization and adaptability across diverse downstream tasks， thereby drawing increasing attention from the research community. Within the domain of remote sensing （RS）， data exhibit significant heterogeneity across multiple sources， modalities， spatial scales， and temporal dimensions. Designing pretrained RS foundation models （RSFMs） capable of effectively capturing such complex geospatial dependencies is critical for robust feature representation and intelligent interpretation of RS imagery. This paper presents a comprehensive review of the recent progress in pretraining strategies for RSFMs by emphasizing unimodal and multimodal learning paradigms. For unimodal models， representative frameworks based on self-supervised contrastive learning and masked image modeling are summarized. They leverage large-scale optical， hyperspectral， and radar imagery to learn transferable visual representations. These pretraining methods substantially enhance downstream performance in land cover classification， object detection， semantic segmentation， and change detection tasks. For multimodal models， we analyze the integration of image-text， image-location， and image-audio modalities through contrastive alignment strategies and cross-modal embedding learning， thereby effectively improving semantic coherence， generalizability， and interpretability in geospatial representation learning. Furthermore， widely adopted RS pretraining datasets， including their data sources， modality compositions， spatial resolutions， and annotation characteristics， are systematically summarized in this paper. Representative datasets， such as BigEarthNet， SEN12MS， and SkySenseGPT， are reviewed to demonstrate the diversity and scale of existing data resources. The importance of building open， standardized， and reproducible data repositories is emphasized， as these datasets serve as the foundation for training scalable and generalizable RSFMs. From a methodological perspective， this paper discusses the major pretraining paradigms that have shaped the current landscape of RSFMs， including contrastive self-supervised learning， generative self-supervised learning， and hybrid teacher-student distillation. These paradigms aim to maximize representational consistency between augmented views， reconstruct masked information， and align intermediate features between models， thereby enabling the extraction of semantically rich and transferable geospatial features. Despite these advances， several challenges remain unresolved in the development of RSFMs. Data-related issues， such as the scarcity of well-annotated multimodal datasets， geographic and temporal imbalance， and high acquisition costs， continue to hinder large-scale model training. Model scalability poses another limitation， as the billion-parameter-level architectures demand extensive computational resources and energy consumption during training and inference. Moreover， current RSFMs often suffer from limited cross-domain and cross-sensor generalization， thereby leading to performance degradation when applied to new regions or modalities. Transparency and interpretability also remain pressing concerns， as understanding the internal mechanisms of deep RSFMs and improving their robustness against adversarial perturbations are essential for reliable real-world deployment. Future research may address these challenges by focusing on developing scalable multimodal architectures that can jointly process optical， synthetic aperture radar， hyperspectral， and textual data， as well as by designing lightweight RSFMs through model compression， sparse training， and modular architecture optimization. Improving cross-domain and cross-temporal generalization by incorporating domain adaptation， meta-learning， and transfer learning techniques will further enhance model robustness under diverse acquisition conditions. In addition， integrating explainable artificial intelligence approaches， uncertainty quantification， and attention-based visualization can improve the interpretability and trustworthiness of RSFMs， thereby enabling their safe application in operational RS systems. Overall， this paper provides a systematic and forward-looking overview of the current development status， pretraining methodologies， benchmark datasets， and existing challenges of RSFMs. This work aims to offer a theoretical and methodological reference for the future construction of intelligent， scalable， and trustworthy foundation models in the RS domain by consolidating advances in unimodal and multimodal pretraining paradigms.

ObjectiveCharts are a vital tool for information presentation in research and business analysis， offering a clear and intuitive understanding of complex data relationships and trends. However， the inability to access the underlying raw data of these visual representations creates a serious barrier for in-depth analysis and further data utilization. Chart data extraction （CDE） technology aims to bridge this gap by accurately extracting data from visual charts and converting them into structured numerical values or tables， enabling complex metric calculations， chart type conversions， and other downstream tasks.MethodThis study addresses the challenges of CDE in the Chinese language context. We present the construction of the large-scale Chinese bar chart dataset， which contains 58 712 images encompassing various types， including vertical， horizontal， and stacked bar charts， with complex scenarios （e.g.， multiangle text rotation）. We derive specialized datasets for chart text recognition， text classification， and legend detection， providing a robust foundation for Chinese chart understanding tasks. To evaluate and compare different CDE approaches， we propose two benchmark models. Rule-based chart data extraction： this method combines text recognition differentiable binarization net （DBNet） and object detection you only look once version 5 （YOLOv5） algorithms to extract textual elements and graphical features. A rule-based library， which is designed based on chart structure characteristics， is then employed for data structure reconstruction. Large model fine-tuning for chart data extraction： this approach utilizes a pretrained large vision-language model （i.e.， Qwen-VL-2B） and adapts it to the CDE task through parameter-efficient fine-tuning low-rank adaptation. This method leverages the model’s general knowledge representation capabilities and enhances its performance on specific tasks with minimal computational resources. To assess the performance of the proposed methods and existing state-of-the-art models， we design and implement a CDE and type conversion system by using PyQt5. This system integrates multiple models， including the rule-based approach， the fine-tuned large model， and other open-source CDE models， enabling users to easily extract data and convert chart types.ResultExperiments conducted on the Chinese bar chart dataset demonstrate the effectiveness of the proposed methods. Among the compared methods， the rule-based approach achieves the best performance overall （F1 score of 69.97%）， particularly for charts without data labels. This success can be attributed to the utilization of optical character recognition and object detection models specifically trained for the dataset， along with the image segmentation and multiscale line sampling true value correction algorithm. On the English Understanding Data Visualization via Question Answering Dataset， the fine-tuned large model outperforms existing state-of-the-art methods， achieving an F1 score of 61.28%. This result highlights the model’s strong generalization capabilities and robustness in cross-lingual scenarios. Qualitative analysis further confirms the effectiveness of the proposed methods. In comparison with other models， our approach demonstrates superior performance in handling complex chart structures and irregular text， accurately extracting metadata from charts. Ablation experiments are also conducted to investigate the contributions of the different components of the large model fine-tuning approach. Results reveal that the combined fine-tuning of the visual encoder， language decoder， and cross-modal adapter achieves excellent performance， indicating the necessity of a holistic optimization strategy for complex CDE tasks.ConclusionThis study presents a comprehensive approach to CDE in the Chinese language context. The construction of a large-scale Chinese bar chart dataset and the proposal of two benchmark models provide valuable resources and reference standards for future research. The developed CDE and type conversion system demonstrates the practical application potential of the proposed methods. The current dataset focuses on bar charts and synthetic data， so future work may explore the integration of real-world charts and additional chart types to enhance dataset realism and diversity. Further research should also be conducted to investigate other robust model structures and training methods to address the limitations of large models in managing complex chart structures and irregular text. The developed system can be further expanded with functionalities， such as supporting other chart type conversions and chart style modifications. In conclusion， this study provides a substantial contribution to the field of Chinese CDE by offering new solutions and promoting the development of multimodal data analysis. We believe that further research and development in this area will unlock the full potential of chart data and enable efficient and insightful data analyses in various domains.The dataset is linked at https://doi.org/10.57760/sciencedb.j00240.00052. The code is linked at https://github.com/maqiuping59/ChineseChartExtract.

ObjectiveMotion deblurring is an important yet complex task in image restoration. In many practical scenarios， images can exhibit complex motion blur because of camera shake， rapidly moving objects， or changes in shooting conditions. This type of blur often has nonuniform characteristics， resulting in a substantial loss of high-frequency details in the images. High-frequency details are crucial for image clarity， recognition， and visual experience， making their restoration a research hotspot in image processing. However， existing deblurring methods typically perform poorly when confronted with these complexities， and the restored images often suffer from noticeable blurriness and detail loss. This phenomenon not only affects image quality but also limits performance in high-precision application scenarios， such as video surveillance， autonomous driving， and medical imaging. Therefore， designing a novel， effective motion image deblurring method that can address complex nonuniform blur situations has become an urgent issue that needs resolution.MethodA motion image deblurring method that is based on a Transformer architecture and multiscale feature fusion is proposed in this study to resolve the challenges of motion deblurring. This method utilizes an encoder-decoder network structure， which is widely used in deep learning because of its excellent feature extraction and restoration capabilities in image tasks. In the encoder part， a module that combines convolutional neural networks （CNNs） and a dual-attention mechanism is designed. The advantage of CNNs is their ability to effectively extract local features from images. The dual-attention mechanism guides the model’s focus to important areas in the image. This combination not only enhances the depth and breadth of feature extraction but also substantially improves the model’s ability to capture key details， thereby providing a solid foundation for subsequent deblurring tasks. In the feature fusion section， a multiscale feature fusion module is employed to enhance feature expressiveness through a gated deep convolutional feedforward network and a feature enhancement module. The multiscale feature fusion strategy allows the network to integrate feature information from different scales， ensuring that details are preserved during the restoration process and effectively addressing varying degrees of blur across different regions of the image. The feature enhancement module extracts high-frequency information to enhance the clarity and realism of the final image， ensuring that critical details are not lost during reconstruction. In the decoder part， a modified Transformer module is introduced. Fourier transform is integrated into the feature extraction process within the feedforward network layer to enhance frequency-domain information. Fourier transform aids in processing images in the frequency domain， providing rich frequency information and effectively improving the model’s ability to recover subtle features in blurred images. This innovative improvement offers a new approach to feature processing， allowing for a high level of restoration performance in actual deblurring tasks.ResultTo evaluate the effectiveness of the proposed method， extensive comparative experiments are conducted on GoPro and Human-Centric Indoor Deblurring （HIDE） datasets， and in-depth comparisons with existing mainstream methods are performed. Experimental results indicate that the proposed method achieves considerable advantages across multiple metrics. In the GoPro dataset， the peak signal-to-noise ratio （PSNR） reaches 32.70 dB， and the structural similarity index measure （SSIM） is 0.954， indicating that the quality of the restored images is very high， and details are largely recovered. In the HIDE dataset， PSNR reaches 30.53 dB， with an SSIM of 0.922， further validating the effectiveness and adaptability of the proposed method. Ablation experiments are performed to provide in-depth analysis of each module’s contributions， verifying the positive effect of the proposed innovative points on motion image deblurring. These experiments clearly demonstrate the unique roles of each module and provide quantitative evidence that confirms the effectiveness of multiscale feature fusion and the Transformer module.ConclusionThe motion deblurring method that is based on the Transformer architecture and multiscale feature fusion substantially outperforms existing mainstream methods in experiments conducted on GoPro and HIDE datasets. This research provides a novel solution to the critical issue of motion image deblurring and showcases the potential applicability of the proposed method in practical scenarios， particularly in image processing， video surveillance， and autonomous driving. Future research efforts could focus on extending the principles underlying this method to other image restoration tasks， such as denoising and super-resolution， further enhancing the model’s versatility and robustness. Moreover， exploring the incorporation of domain-specific knowledge and prior information related to the nature of blur into the deblurring process could guide the model toward achieving highly favorable outcomes in real-world applications. Continual optimization and refinement of this innovative approach are expected to create opportunities in the field of image restoration. By doing so， this method has the potential to substantially contribute to the advancement of related technologies and applications， including but not limited to improving image quality in photography and enhancing the performance of image processing systems used in various industries. In summary， the proposed motion deblurring method represents a remarkable advancement in the ongoing effort to overcome the challenges posed by motion blur in images. With the advent of deep learning techniques and modern image processing methodologies， these advancements can be leveraged to develop solutions that directly address the limitations of existing approaches. Whether utilized to enhance the quality of consumer photography， improve security camera footage， or refine images captured in autonomous driving systems， successful motion deblurring techniques have far-reaching implications. The link is https://doi.org/10.57760/sciencedb.j00240.00170 and https://github.com/zh7546/project.git.

ObjectiveIn the tasks of 3D scene reconstruction and novel view synthesis， neural radiance fields （NeRF） stands as a landmark advancement in this field. It significantly drives the development and evolution of neural rendering-based 3D scene reconstruction methods. Moreover， 3D Gaussian splatting （3DGS） has emerged as a simple yet computationally efficient approach， thereby gaining recognition for its fast training and rendering capabilities. However， these methods typically operate only under ideal input conditions and often struggle to handle motion blur——a critical issue， given that motion blur in input images severely degrades rendering quality. Some existing methods attempt to address this issue by employing trainable parameters or models during reconstruction to predict the trajectory of camera motion blur. Nevertheless， this predictive approach fails to reflect the real trajectory of camera movement accurately， thereby limiting the accuracy of 3D scene reconstruction. Other methods integrate deblurring models with the 3DGS framework to accelerate model training and rendering speeds. Regrettably， these methods suffer from quality degradation issues， such as artifacts when rendering novel views， because of the risk of model overfitting during training. This study proposes a novel method that fuses event camera data with the 3DGS framework to overcome the limitations of the aforementioned approaches.MethodAn event camera triggers event responses only when the pixel brightness changes beyond a preset threshold， thereby enabling it to capture the instantaneous motion trajectories of objects and subtle brightness fluctuations in dynamic scenes accurately. The proposed method fully leverages the unique advantage of event cameras——microsecond-level high temporal resolution. For the sequential grayscale images output by the model， the method designs an event bin calculation module： first， it divides the grayscale image sequence at fixed time intervals and defines the time window corresponding to two consecutive frames as an event bin unit. Subsequently， it counts the number of events triggered by brightness changes for each pixel within this unit， thereby generating event bin data predicted by the model. The method further introduces real event bins as supervision signals to ensure the effectiveness of event information supervision. Real event bins are constructed using synchronously collected raw event camera data， and the pixel-level L2 error between the predicted event bins and the real event bins is calculated. Then， this error is incorporated into the model’s loss function. This supervision mechanism forces the model to learn the laws of the continuous blurring process at high temporal resolution， thereby avoiding traditional methods’ problem of losing details of dynamic processes because of reliance on discrete frame images. The information loss of traditional frame images in dynamic scenes can be compensated by combining the event bin calculation module with the 3DGS framework， thereby providing rich spatiotemporal constraints for deblurring reconstruction. Second， the method innovatively introduces a Gaussian shape attribute transformation network， which establishes a prediction and adjustment mechanism for attributes such as scaling and rotation of each 3D Gaussian primitive in the 3DGS framework. During the model training phase， this transformation mechanism achieves dynamic optimization of Gaussian primitives through iterative adjustments： after each training epoch， the parameters of all Gaussian primitives are updated based on the transformation attributes predicted by the network， gradually correcting the deviation between the initial Gaussian primitives and the real scene structure. This approach effectively prevents the Gaussian model from overfitting to the training views. During the rendering process， these transformation attributes can dynamically adjust the spatial distribution of 3D Gaussian primitives in real time， thereby allowing the Gaussian primitives to conform to the motion states and geometric structures of dynamic objects in the scene. As a result， the constraints of the original view data on the model are broken， and the degradation of rendering quality caused by geometric mismatches is avoided when switching to novel views.ResultExperimental results demonstrate the superiority of the proposed method. On synthetic datasets， it outperforms the existing methods in terms of peak signal-to-noise ratio， structural similarity index， and learned perceptual image patch similarity. This finding indicates a significant reduction in image noise and blur， and the rendered results exhibit structural information （such as edges and textures） that is highly consistent with real scenes. On real-world datasets， the method achieves a substantial reduction in the blind/referenceless image spatial quality evaluator value， thereby fully verifying the method’s deblurring capability in real and complex scenarios. Compared with NeRF-based methods， the proposed method reduces the training time from 48 h to less than 1 h （representing an approximately 50-fold improvement in training efficiency） and achieves a real-time rendering speed of 140 frames/s.ConclusionComprehensive experimental results confirm that the proposed method addresses the shortcomings of existing neural rendering-based deblurring reconstruction techniques in novel view generalization ability. It also achieves dual improvements in image clarity and computational efficiency through innovations in data fusion and network structure. This method provides a practical technical solution for high-fidelity and real-time reconstruction of dynamic scenes.

ObjectiveThe rapid advancement of deep learning techniques has resulted in significant progress in the field of image inpainting， particularly in utilizing semantic structures to guide the inpainting process. Semantic structures， such as semantic label maps， have become increasingly popular because of their ability to provide valuable contextual information about missing or damaged regions of images. These semantic structures help guide the inpainting algorithm to restore missing content in a way that aligns well with the overall context and semantic meaning of the image. Traditional inpainting models often rely on pixel-level information and texture-based techniques to reconstruct missing regions. However， they may struggle to maintain consistent semantic structures， particularly when dealing with complex images or large damaged areas. Recent advances have shown that combining semantic information with inpainting tasks can significantly improve the quality and realism of image restoration. In particular， semantic segmentation， which can be leveraged to guide the inpainting process， has become an essential tool for extracting meaningful context from an image. Although semantic segmentation models provide rich， semantic information that is crucial for accurate image inpainting， many existing methods remain unidirectional. These models often rely solely on pretrained semantic segmentation networks to provide guidance， without considering feedback from the inpainting model itself to improve or refine the segmentation. This limitation can prevent the model from fully correcting semantic errors， resulting in inadequately accurate inpainting outcomes. We propose a novel image inpainting method that introduces feedback correction within a semisupervised learning framework to overcome the aforementioned challenges. This approach allows for a bidirectional interaction between the inpainting and segmentation models， thereby enabling them to refine each other iteratively and ultimately enhance the final inpainting results.MethodThe proposed method follows a three-stage progressive image inpainting algorithm， which includes coarse inpainting， semisupervised semantic correction， and fine inpainting. Each of these stages plays a critical role in improving the quality and accuracy of the final image restoration， particularly in scenarios where semantic biases and texture errors are prevalent.In the first stage， coarse inpainting is performed. The goal of this module is to create an initial pixel-level reconstruction of the missing areas. At this stage， the algorithm focuses on restoring basic structures and textures within the damaged regions. Although the inpainting process may still introduce some semantic errors or distortions， it provides a solid foundation for further refinement. This coarse inpainting serves as the basis for the following stages， which aim to address semantic inconsistencies and improve texture details.The second stage involves semisupervised semantic correction. Here， we leverage a cross-image semantic consistency strategy to enhance the semantic understanding of the damaged regions and improve the accuracy of segmentation. This module employs semisupervised learning techniques to generate pseudo-labels for unlabeled images， which are then used to refine the semantic segmentation of the inpainted areas. The segmentation network can correct any semantic errors introduced during the initial inpainting stage by incorporating feedback from the inpainting model. This feedback loop helps ensure that the inpainted regions align closely with the rest of the image in terms of texture and semantic content.The third and final stage is fine inpainting. In this stage， the semantic segmentation model——now trained with the refined semantic labels——guides the inpainting model to restore the missing content with high precision. The improved semantic labels allow the inpainting model to generate accurate pixel-level details and textures， thereby ensuring that the final result is semantically consistent with the original image. This stage refines the image restoration process by incorporating the corrected segmentation and the inpainted content， thereby resulting in a realistic and seamless reconstruction.ResultWe conducted extensive experiments using two publicly available datasets， namely， CelebA-HQ and Cityscapes， to validate the effectiveness of the proposed method. The evaluation was based on several commonly used metrics， including learned perceptual image patch similarity （LPIPS）， peak signal-to-noise ratio （PSNR）， and structural similarity index measure （SSIM）. These metrics are designed to assess the perceptual quality， structural consistency， and overall fidelity of the inpainted images. The experimental results demonstrate that the proposed method outperforms existing inpainting techniques. On the CelebA-HQ dataset， our approach achieves a 5.88% reduction in LPIPS， a 0.52% increase in PSNR， and a significant improvement in SSIM， thereby indicating a notable enhancement in perceptual quality. The results on the Cityscapes dataset are even more impressive than those on the CelebA-HQ dataset， with the LPIPS decreasing by 6.15%， the SSIM increasing by 1.58%， and the PSNR values remaining superior to those of the other methods. These improvements highlight the effectiveness of the proposed feedback correction mechanism， which helps address semantic biases and texture errors in the inpainting process. Ablation studies were also conducted to confirm the contribution of the semantic correction mechanism further. Results show that the integration of semantic correction significantly enhances the inpainting quality， thereby validating the importance of the interaction between the inpainting and segmentation models.ConclusionThis study provides new insights into the synergy between semantic segmentation and image inpainting. The proposed method addresses semantic inconsistencies and texture errors more effectively than previous approaches by fostering an interactive relationship between the two models. Experimental results demonstrate that the feedback correction mechanism， implemented within a semisupervised learning framework， significantly improves the overall performance of image inpainting tasks. This approach opens new possibilities for high-quality image restoration， particularly in applications where semantic consistency is crucial for achieving realistic and contextually accurate results.

ObjectiveSingle-image super-resolution （SISR） focuses on generating a high-resolution （HR） image from a single low-resolution （LR） input by recovering the texture and structural details that are often lost during image degradation. In recent years， SISR has been widely applied in various domains， including computer vision， image processing， and public surveillance. Current SISR methods include traditional approaches——such as interpolation， reconstruction， and example-based techniques——that rely on prior knowledge but often struggle to recover fine details and textures. By contrast， deep learning-based methods， particularly convolutional neural networks， have made significant progress. However， many of these methods improve performance primarily by increasing network depth， which introduces feature redundancy and inefficient utilization of multilayer features， thereby resulting in artifacts such as texture distortion and edge inconsistency. This work addresses these challenges by proposing an adaptive feature fusion recursive network （AFFRN）， which incorporates an adaptive feature fusion module （AFFM） consisting of three specialized branches. AFFRN facilitates efficient information propagation and progressive feature enhancement by employing a recursive refinement mechanism； thus， it can significantly improve texture reconstruction quality.MethodThe proposed AFFRN consists of three main stages. First， the input LR image is processed by several convolutional layers to extract initial low-level representations， which primarily capture basic edge and texture information. These feature maps are passed through AFFM， which performs dynamic and depth-aware fusion of multilevel features. Within AFFM， three specialized branches work in parallel to extract and integrate complementary feature representations： 1） The detail attention branch （DAB）， which focuses on enhancing shallow-layer features using attention mechanisms to preserve fine textures and edges； 2） The detail exploration branch （DEB）， which captures deep contextual information through densely connected blocks combined with downsampling and upsampling to enable modeling of long-range dependencies and global structures； 3） The weight assignment branch （WAB）， which， as the core innovation， adaptively assigns weights to features from DAB and DEB based on their relative importance to facilitate selective fusion that suppresses redundancy and emphasizes discriminative information. Moreover， AFFM is integrated into a recursive framework， where the fused features from each iteration are fed back into the module for subsequent refinement. This recursive refinement， which is critical for accurate super-resolution reconstruction， facilitates coarse-to-fine structural recovery and gradual enhancement of hierarchical features. Finally， the aggregated and enhanced feature representations are passed through convolutional and transposed convolutional layers to restore spatial resolution and reconstruct the output HR image. This design allows AFFRN to balance depth， efficiency， and representational power effectively， thereby resulting in superior performance on texture-rich and structurally complex image regions.ResultAFFRN is trained on 900 images selected from the DIV2K dataset， with data augmentation being performed through random horizontal and vertical flips to enhance data diversity. Training is performed using the Adam optimizer， with L1 loss serving as the loss function. Performance is evaluated on five public benchmark datasets： Set5， Set14， BSD100， Urban100， and Manga109. HR images are downsampled using bicubic interpolation with scale factors of ×2， ×3， and ×4 to simulate LR inputs. The Y channel of the YCbCr color space is used for evaluation， with peak signal-to-noise ratio （PSNR） and structural similarity index （SSIM） serving as the assessment metrics. AFFRN is compared with 19 representative SISR methods. Experimental results demonstrate that AFFRN achieves the highest average PSNR and SSIM values across all scaling factors. Moreover， it recovers highly accurate textures and fine structural details. On the Urban100 dataset， AFFRN achieves PSNR gains of 0.75 dB， 0.53 dB， and 0.54 dB over AFSMNet at scaling factors of ×2， ×3， and ×4， respectively. On the Manga109 dataset， the proposed method also outperforms AFSMNet by 0.11 dB， 0.17 dB， and 0.38 dB at the same scales. Ablation studies further validate the effectiveness and necessity of each module within the proposed network. Removing the core WAB from AFFM results in a PSNR decrease of 0.24 dB， thereby emphasizing the critical role of the proposed WAB in performance improvement.ConclusionThis study proposes AFFRN， a network specifically designed for the SISR task. The architecture integrates a recursive learning strategy with an AFFM， which plays a critical role in progressively enhancing feature representations across multiple depths. Within the AFFM， the DAB is responsible for refining shallow features to preserve high-frequency textures， whereas the DEB captures deep semantic information by modeling global contextual dependencies. WAB adaptively allocates importance weights to merge these complementary features effectively， thereby promoting informative feature integration while suppressing redundancy. The recursive framework further strengthens feature propagation by enabling iterative refinement， thereby allowing the model to improve detail recovery progressively over multiple stages. Comprehensive evaluations on five benchmark datasets demonstrate that， compared with existing state-of-the-art methods， AFFRN achieves competitive performance in quantitative metrics and visual quality. Ablation studies further validate the necessity and effectiveness of each component， especially highlighting the critical role of the WAB in enhancing reconstruction accuracy. These results confirm that the proposed adaptive multibranch fusion combined with recursive refinement can effectively alleviate feature redundancy and inefficiency issues， thereby leading to rich texture recovery and highly accurate super-resolution results.

ObjectiveTexture filtering is a fundamental task in computer vision that attempts to preserve significant structures and smooth out irrelevant textures in an image through filtering. It is useful for many image analysis and processing tasks， such as contour detection， image segmentation， image abstraction， detail enhancement， tone mapping， and image denoising. Existing texture filtering algorithms can be mainly divided into traditional filtering algorithms， such as algorithms based on local and global optimization， and neural network filtering algorithms. Traditional filtering algorithms can hardly use high-level semantics in images and rely heavily on manual parameter adjustment， whereas neural network filtering algorithms achieve better results with the help of the powerful feature representation ability of convolutional neural networks （CNNs）. However， neural network filtering algorithms have fixed inference parameters after training. Textures and structures of varying scales， which require different filtering strengths， exist in images； adaptively adjusting and smoothing large-scale and strong-gradient textures while maintaining small-scale and weak-gradient structures are difficult for a neural network with fixed parameters. In addition， high-quality texture filtering datasets for training neural networks are lacking， and the common practice is to select the filtering results of existing algorithms as imperfect labels or to make synthetic structure-texture images with domain shift from real scene images. However， all of them have flaws， resulting in poor generalization performance of the algorithm on real scene images. To address the issue of the filtering algorithm’s lack of adaptive filtering capability， this study proposes a two-stage dynamic side window filtering kernel prediction network （SWNet）. It employs reverse thinking and a divide-and-conquer strategy， which involves smoothing first and then reconstructing， suppressing large-scale textures， distinguishing between structural and texture pixels during reconstruction， and restoring clear structures. For the issue of domain shift in the dataset， an improved structure-texture image synthesis method is proposed， and a hybrid synthetic texture filtering （HSTF） dataset is made.MethodWe first design a hierarchical U-Net encoding-decoding module based on transformers and CNNs to generate a structure area segmentation map and an oversmooth image. Then， based on guided filtering， side window filtering， and dynamic convolution， a filter kernel prediction module is designed to predict the sampling points and weight values of eight groups of side window filter kernels under the guidance of the structure area segmentation map， the oversmooth image， and the original input image. Afterward， the oversmooth image is sampled and filtered. Finally， a linear fusion is used to obtain the filtering result. The HSTF dataset consists of two parts： a filling subdataset that fills different texture maps in the segmented area and a fusion subdataset that fuses a structure background map with a single texture map. They are combined for network training.ResultIn the experiment， the SWNet algorithm is compared with 18 algorithms， including classical and latest algorithms， on 6 datasets. Compared with the second-best algorithm on the test set of the HSTF dataset， the SWNet algorithm improves the peak signal-to-noise ratio（PSNR） value by 2.581 dB and the structure similarity index measure（SSIM） value by 0.033. On the benchmark for edge-preserving image smoothing（BEPS） dataset， the SWNet algorithm improves the PSNR value by 4.616 dB and the SSIM value by 0.090. On the nankai smoothing dataset（NKS） dataset， it increases the PSNR value by 1.942 dB and the SSIM value by 0.012. On the structure-preserving image smoothing（SPS） dataset， SWNet raises the PSNR value by 2.701 dB and the SSIM value by 0.034. Meanwhile， a visual effect comparison experiment is carried out on the RTV， BEPS， and ImageNet2012 datasets to intuitively verify the effectiveness of the SWNet algorithm from the viewpoint of human eye perception. Unlike other algorithms， the SWNet algorithm presents a finer structure preservation and smoother texture area （no remnant texture and close to a flat pure color）. We also retrain some classical algorithms on the original dataset， the filling subdataset， the fusion subdataset， and the HSTF dataset. The comparative experiment shows that the HSTF dataset can effectively improve the structure preservation and texture smoothing effect of the algorithm on real scene images.ConclusionThe SWNet algorithm proposed in this paper combines the advantages of global dependence capture of transformers and low-level feature extraction of CNNs. It uses an oversmooth image and a structure area segmentation map to guide the smoothing and preservation processes. With the help of the strong structure-preserving ability of the side window filter kernel and the design of dynamic sampling points and weight values， SWNet can effectively smooth out different scale textures while maintaining the significant and narrow structures， thereby realizing scale-adaptive filtering. Moreover， the HSTF dataset considers the structure and texture pattern of real scene images， which can effectively improve the generalization performance of the algorithm on real scene images.

ObjectiveDriven by standards such as moving picture expert group （MPEG）， H.26x， audio video coding standard （AVS）， and AOMedia video （AVx）， video coding technology has evolved significantly over the past 3 decades. These standards commonly adopt a block-based hybrid framework that improves compression efficiency through spatial/temporal prediction， adaptive entropy coding， and rate-distortion optimization. However， lossy compression inevitably introduces artifacts such as blurring， blocking， and ringing. Traditional encoders address these issues using hand-crafted filters， such as a deblocking filter， a sample adaptive offset， an adaptive loop filter， a constrained directional enhancement filter， and loop restoration. Deep neural networks， offering superior performance due to their nonlinear representation power， have been recently introduced to replace rule-based filters. However， existing models often require large parameter counts and substantial memory， thereby limiting their deployment on mobile devices. This work addresses these challenges by introducing a compact， deployment-friendly neural network loop filter tailored for mobile and edge environments.MethodWe propose a lightweight and deployment-friendly approach built on a U-Net backbone to tackle the challenges of high memory consumption， slow inference speed， and poor hardware compatibility in neural network-based loop filtering. The network adopts an encoder-decoder structure with progressive downsampling to reduce interblock complexity and overall computational cost. Structural reparameterization is employed to optimize the network further for deployment： During training， multibranch convolutions enhance feature representation capacity， whereas at inference， they are merged into single-branch standard convolutions to minimize operator dependencies， simplify execution， and reduce intermediate memory usage. A key challenge of U-Net architectures is the memory overhead caused by long skip connections， given that feature maps from each encoder stage must be stored for later use. We address this bottleneck by designing a multiscale feature fusion and compression module. First， encoder features from all stages are compressed via pointwise convolutions and spatially aligned at the smallest resolution using downsampling. Then， these features are concatenated along the channel dimension and undergo a second compression stage to generate a compact and expressive representation. In the decoder， this representation is reconstructed to its original resolution and further enhanced using reparameterized convolutional blocks. This design significantly reduces peak memory consumption while preserving feature fidelity and improving filtering quality， effectively achieving a joint optimization of memory footprint， inference speed， and reconstruction performance. The entire network is trained end-to-end by taking image residuals as input and using mean squared error as the loss function. Finally， the trained model is embedded into the AOMedia Video 1（AV1） encoder pipeline and combined with an R-D cost optimization strategy to select the optimal filtered frames adaptively during the loop filtering stage， thereby improving overall coding efficiency under real-world video coding scenarios.ResultExtensive experiments conducted within the AV1encoding framework demonstrate that the proposed method achieves a -5.09% BD-rate （Bjøntegaard delta rate） with 77 frames/s at 1 080 P in intraframe prediction and a -4.32% BD-rate in interframe prediction， thereby providing a quantitative evaluation of its performance. In particular， under intraframe prediction， our model attains an average BD-rate reduction of -5.09%. This finding indicates that our model outperforms most competing methods， such as variable-filter-size residue learning convolutional neural network （VRCNN） （-3.48%）， MobileNetV2 （-2.66%）， and RepSR （re-parameterization super resolution） （-3.81%）. Moreover， it achieves -5.52% and -6.75% in ClassA1 （4 K） and ClassA3 （720 P）， respectively， thereby demonstrating strong performance across both high- and mid-resolution content. Under interframe prediction， the proposed method maintains competitive performance with an average BD-rate reduction of -4.32%， closely matching the average BD-rate reduction of deep semi-smooth newton network （DeepSN-Net） （-4.35%） while exhibiting stable results across test sequences. For instance， in ClassA1， our model attains a larger gain （-4.61%） than all other baselines. In ClassA3， where DeepSN-Net achieves the highest gain （-6.23%）， our model still performs comparably （-5.77%） with low fluctuation across resolutions. Moreover， the model’s peak memory consumption during the M_sc3 （memory of skip connection 3） stage is reduced from 27.69 MB to 1.73 MB， indicating a 93.75% decrease， by adopting the feature compression strategy with compression coefficients α₁ = α₂ = α₃ = β = 0.5. This substantial reduction is critical for deployment on resource-constrained platforms. When deployed on a Snapdragon 8 Gen1 neural processing unit （NPU）， the model processes 1 080 P video at 77 frames/s with a peak memory usage of only 55 MB. This finding confirms the model’s suitability for real-time mobile inference under strict memory and latency constraints.ConclusionThe lightweight neural network loop filtering method proposed in this work effectively balances compression efficiency， memory footprint， and inference speed， thereby delivering an integrated solution tailored for mobile and edge device deployment. Compared with the existing state-of-the-art neural network video coding models， our approach achieves competitive or superior coding gains while drastically reducing resource consumption， particularly in peak memory usage and inference latency. This combination of high performance and deployment friendliness makes the proposed method highly suitable for real-time video processing scenarios on mobile devices equipped with neural network accelerators. Our results validate the practical viability of structurally reparameterized convolution modules combined with feature fusion compression in mitigating memory bottlenecks and accelerating inference without sacrificing reconstruction quality. This work provides a promising direction for advancing neural network video coding technologies toward widespread adoption in mobile and edge computing environments to enable enhanced video quality and efficient bandwidth utilization with low power and resource demands.

ObjectiveThe propagation of misinformation has emerged as a significant challenge in the digital age. Traditional multimodal fake news detection research has primarily focused on the binary classification task of content authenticity； for instance， the capabilities for identifying specific types of tampering and localizing tampered regions are lacking. Fake content that combines visual and textual modalities in cyberspace has proliferated rapidly with the rapid advancement of multimedia technologies. Despite some progress in multimodal media tampering detection and localization， existing studies commonly face critical issues， including insufficient cross-modal hierarchical information interaction and limited accuracy in localizing tampered regions. This study addresses these challenges by proposing a tampering detection framework based on multiview visual-language information interaction.MethodThe proposed method is designed to tackle the key challenges in multimodal deep forgery detection and localization by leveraging multiple perspectives of visual-language interaction. First， a hierarchical tampering contrastive learning mechanism is constructed using global and local dual-view feature embedding. This mechanism achieves fine-grained cross-modal semantic alignment， effectively capturing the semantic inconsistency in tampered regions. The approach innovatively integrates multiple strategies to enhance the model’s ability to detect and localize forgeries. Second， a forgery perception interaction module is designed. It incorporates multiscale feature extraction and frequency-domain feature fusion techniques. In this way， the localization capability of tampered features at different granularities is significantly improved. In addition， a cross-modal gating fusion module is introduced to optimize the information interaction between modalities. This approach is achieved through a dynamic weight allocation strategy that enhances the model’s discriminative power in multimodal deep forgery detection and fine-grained classification tasks. The proposed method utilizes advanced techniques in deep learning， including transformers and bidirectional encoder representations from transformers （BERT）， to achieve fine-grained alignment and tampering detection. Vision transformers （ViTs） are used for image feature extraction， whereas BERT is used for extracting textual features. The embedded features are subjected to tampering contrastive learning from global and local perspectives. Unlike conventional methods， which typically only pull close-matching image-text pairs and push away nonmatching pairs， the proposed approach employs the InfoNCE loss function to push away tampered image-text pairs simultaneously， thereby reinforcing their semantic inconsistency. This method enhances the model’s ability to distinguish between tampered and nontampered content， thereby addressing the critical issue of cross-modal alignment in traditional methods. Moreover， a dual-stream cross-modal attention mechanism is utilized to facilitate deep-level information interaction between visual and textual modalities. This mechanism enables the model to capture detailed information from both modalities and improve the overall detection accuracy. In addition to the attention mechanism， the forgery perception interaction module and cross-modal gating fusion module further refine the model’s ability to detect subtle discrepancies in content across different modalities. These modules collectively improve the detection of fine-grained tampering， which is crucial for accurately localizing tampered regions in multimodal content. A multitask learning framework is employed to handle various downstream tasks simultaneously， including bounding box regression， binary classification， multilabel classification， and token-level tampering detection. The multitask learning module consists of a set of lightweight multilayer perceptrons （MLPs） designed to handle these tasks efficiently. The model can simultaneously learn from various types of labeled data and generalize across different types of tasks by employing a multitask learning approach， thereby providing a comprehensive solution for multimodal deep forgery detection. Specific Implementation： The proposed framework is implemented on a high-performance computing environment using the DGM⁴ dataset. The model is trained for 50 epochs on an NVIDIA RTX 4090 GPU， leveraging the PyTorch framework. The dataset used for training contains various multimodal content， including images and corresponding text， which is essential for evaluating the model’s effectiveness in detecting and localizing tampered content across different modalities. ViT is used for extracting image features， which allows the model to process visual data efficiently and capture detailed spatial information. BERT， a powerful transformer-based model for natural language processing， is used for text feature extraction. Then， these features are embedded into a shared space， where tampering contrastive learning is applied. The contrastive learning mechanism ensures that the embeddings of tampered image-text pairs are pushed away from the embeddings of authentic image-text pairs， thereby reinforcing the semantic inconsistencies in tampered regions. This fine-grained contrastive learning approach allows the model to identify subtle discrepancies in both the image and text modalities， thereby improving the detection of tampered regions. In terms of information interaction， the dual-stream cross-modal attention mechanism is employed to allow for deep interaction between the visual and textual features. This mechanism facilitates the exchange of information between the two modalities， thereby enabling the model to capture complex relationships between the image and the text. The forgery perception interaction module further enhances the model’s ability to detect subtle tampering by integrating multiscale features and fusing frequency-domain information. This approach enables the model to capture tampering features at different scales and granularities， thereby improving its ability to localize tampered regions effectively. The cross-modal gating fusion module is another crucial component of the proposed framework. It optimizes the interaction between modalities by using a dynamic weight allocation strategy. This strategy ensures that the most relevant features from each modality are given high importance during the decision-making process， thereby improving the model’s discriminative power. This module is particularly effective in scenarios where the tampered content is subtle or involves complex interactions between visual and textual elements. The multitask learning module， consisting of lightweight MLPs， is designed to support various downstream tasks. These tasks include bounding box regression for localizing tampered regions， binary classification for distinguishing tampered and authentic content， multilabel classification for handling multiple types of tampering， and token-level tampering detection for identifying tampered tokens in text. The multitask framework allows the model to learn from multiple sources of supervision and generalize across different tasks， thereby improving its overall performance.ResultExperimental results demonstrate that the proposed model outperforms existing approaches， such as the hierarchical multimodal manipulation reasoning transformer framework based on hierarchical reasoning， in multimodal deep forgery localization tasks. In the image deep forgery localization task， the model achieves a 6.41% improvement in the intersection over union at a threshold of 75% （IoU75） metric， thereby indicating a significant enhancement in the precision of localized tampered regions. In text tampering localization tasks， the model improves the recall and F1 scores by 5.63% and 2.01%， respectively， thereby demonstrating its superior ability to detect tampered text content. These improvements are a direct result of the fine-grained alignment and deep interaction between visual and textual features enabled by the proposed framework. Compared with the visual-language pretraining with a gate fusion framework， the proposed model exhibits a comprehensive performance advantage in the evaluation of multimodal multitask learning. The model’s ability to handle various tasks simultaneously， coupled with its robust performance in image and text modalities， makes it a highly effective solution for multimodal deep forgery detection and localization.ConclusionThe multiview visual-language information interaction model proposed in this paper exhibits significant superiority over other models in multimodal deep forgery detection and localization tasks， thereby providing a novel technical solution for the multimedia content security field.

ObjectiveIn recent years， domain adaptive object detection has become an important area of research with the growing demand for robust object detection systems across different environmental conditions. Traditional object detectors that use deep learning methods obtain relatively high performance， but they largely rely on large-scale labeled datasets. However， labeling large-scale datasets requires high costs. This challenge motivates the exploration of domain adaptation that aims to maintain good detection performance by learning domain-invariant features or training models with cross-domain generalization ability. Recent studies in domain adaptive object detection have increasingly adopted the mean teacher paradigm， which leverages the stability of the parameters of the teacher model during training to address critical challenges in cross-domain scenarios. The method can enhance the invariance of the model to specific domain shift while preserving the discriminant ability of the feature representations by enforcing the consistency between the predictions of the student model and the outputs of the teacher model. However， these studies often overlook the fact that the student model may learn noisy pseudo-labels generated by the teacher model. This situation can consequently lead to performance degradation. If the teacher model produces a noisy pseudo-label， then it could potentially steer the student model toward an incorrect objective， ultimately leading to the generation of erroneous feedback for the teacher model. Thus， a novel domain adaptive object detection framework named negative teaching and negative learning （NTNL） which integrates a negative teaching module， a negative learning module， and an adaptive weighting mechanism， is proposed in this study to alleviate the aforementioned problem.MethodNTNL not only considers the positive aspects but also incorporates negative thinking； that is， it evaluates the probability that an object does not belong to a certain class. In particular， NTNL introduces the negative teaching strategy to the teacher model， which can iteratively reduce the likelihood of generating incorrect pseudo-labels for uncertain samples， thereby improving the quality of guidance for the student model. Moreover， NTNL employs negative learning such that the student model can be guided to recognize complementary labels. Furthermore， this study designs an adaptive weighting mechanism to address the scale variation of negative teaching due to the different number of classes in diverse tasks. This mechanism can ensure that the proportion of negative teaching loss in the total loss is roughly consistent across different transfer learning tasks.ResultExtensive experiments are conducted on three cross-domain object detection tasks for performance evaluation， including weather adaptation （Cityscapes to Foggy Cityscapes）， cross-style city road adaptation （Cityscapes to Bdd100k）， and real-world to clipart adaptation （Pascal VOC to Clipart1k）. The proposed method NTNL generally obtains better performance than the compared methods. The mean average precision values of NTNL on the above three tasks are 63.4%， 52.5%， and 45.4%， which are 8.0%， 4.7% and 1.7% higher than the mean average precision values of the recent methods， respectively. The proposed NTNL achieves such good performance， probably because it employs the negative teaching and negative learning， both of which help in predicting the class of hard samples correctly. In addition， NTNL can balance the weight of negative teaching in different cross-domain object detection tasks because of the design of the adaptive weighting mechanism， thereby improving the generalization ability of the model. This study conducts a series of ablation experiments， and the resultant findings confirm the effectiveness of each component within the proposed NTNL framework. This study also analyzes the parameter sensitivity to reveal the impact of these parameters on performance. Finally， this study utilizes t-distributed stochastic neighbor embedding （t-SNE） to represent the image features extracted by different models， thereby demonstrating the advantages of the proposed NTNL intuitively. The visualization reveals that the feature clusters generated by NTNL exhibit a high degree of separation， particularly for semantically similar categories， such as riders and people， which are difficult to distinguish； thus， the discriminative effect becomes pronounced.ConclusionThis study proposes a novel domain adaptive object detection method， NTNL， which reduces the impact of noisy pseudo-labels via negative teaching and negative learning. This method also improves the generalization ability of the model with an adaptive weighting mechanism. Comprehensive comparisons with other domain adaptive object detection methods show that the proposed NTNL can achieve superior performance on public cross-domain object detection tasks， thereby proving its validity. Future work will explore incorporating the degree of the domain shift and task difficulty into the adaptive weighting mechanism to improve model generalization further.