浏览全部资源
扫码关注微信
Published： 16 August 2024 ，
移动端阅览
张涛， 王彬沣， 付莹， 刘松荣， 叶吉超， 单培红， 颜成钢. 2024. 基于深度学习的光谱图像超分辨率综述. 中国图象图形学报， 29(08):2113-2136
Zhang Tao， Wang Binfeng， Fu Ying， Liu Songrong， Ye Jichao， Shan Peihong， Yan Chenggang. 2024. Deep learning-based spectral image super-resolution： a survey. Journal of Image and Graphics， 29(08):2113-2136
光谱图像超分辨率技术的目的是从低空间分辨率和光谱分辨率的图像中恢复高空间分辨率和光谱分辨率的图像，在植被调查、地质勘探、环境保护、异常检测和目标追踪等遥感领域有着广泛应用。随着深度学习的兴起，基于深度学习的光谱图像超分辨率算法如雨后春笋般涌现，特别是卷积神经网络、Transformer、生成对抗网络以及深度展开网络等技术的出现使图像融合性能产生了质的飞跃。本文全面探讨和分析了不同光谱图像超分辨率任务场景下的前沿深度学习算法。首先，介绍了光谱图像超分辨率的基本概念，以及不同超分辨率场景的定义。针对单图超分辨率和融合超分辨率两大场景，从超分辨率维度、数据类型、基本框架和监督方式等多个角度详细阐述了各类方法的基本思想和特点。其次，总结了各类算法存在的局限性，并提出了进一步改进的方向。然后，简要介绍了不同融合场景中常用的数据集，并明确了各种评估指标的具体定义。对于每种超分辨率任务，从定性评估、定量评估等多个角度全面比较了代表性算法的性能。最后，总结了研究结果，并探讨了光谱图像超分辨率领域所面临的一些严峻挑战，同时对未来可能的研究方向进行了展望。所提及的算法和数据集已汇总至
https://github.com/ColinTaoZhang/DL-based-spectral-super-resolution
https://github.com/ColinTaoZhang/DL-based-spectral-super-resolution
。
The goal of spectral image super-resolution technology is to recover images with high spatial resolution and spectral resolution from images with low spatial resolution a
nd spectral resolution. Images of high spatial and spectral resolution are widely used in remote sensing fields such as vegetation survey， geological exploration， environmental protection， anomaly detection， and target tracking. With the rise of deep learning， spectral image super-resolution algorithms based on deep learning have emerged. In particular， the emergence of technologies such as end-to-end neural networks， generative adversarial networks， and deep unfolding networks has made a qualitative leap in spectral image super-resolution performance. This study comprehensively discusses and analyzes cutting-edge deep learning algorithms under different spectral image super-resolution task scenarios. First， we introduce the basic concepts of spectral image super-resolution and the definitions of different super-resolution scenarios. Focusing on the two major scenarios of single-image super-resolution and fusion super-resolution， the basic concepts of various methods are elaborated from multiple perspectives such as super-resolution dimensions， super-resolution data types， basic frameworks， and supervision methods， and their characteristics are discussed. Second， this study summarizes the limitations of various algorithms and proposes directions for further improvement. Furthermore， the commonly used datasets in different fusion scenarios are briefly introduced， and the specific definitions of various evaluation indicators are clarified. For each super-resolution task， this study comprehensively compares the performance of representative algorithms from multiple perspectives such as qualitative evaluation and quantitative evaluation. Finally， this study summarizes the research results and discusses some serious challenges faced in the field of spectral image super-resolution， while also looking forward to possible future research directions. First， from the perspective of super-resolution scenarios， the existing spectral image super-resolution algorithms can be divided into two categories， namely， single image su
per-resolution and fusion-based super-resolution. Specifically， single spectral image super-resolution is designed to generate high-resolution output images from a single low-resolution input image. According to the direction of super-resolution， single image super-resolution can be divided into spatial super-resolution， spectral super-resolution， and spatial-spectral super-resolution. Fusion-based spectral image super-resolution is designed to fuse images of different modes into a single image with high spatial and spectral resolution. According to the different modes of fusion images， fusion-based spectral image super-resolution can be divided into pansharpening and multispectral and hyperspectral images fusion. Moreover， deep learning-based spectral image super-resolution methods can be categorized into end-to-end neural network based （E2EN-based） spectral image super-resolution framework， generative adversarial network-based （GAN-based） spectral image super-resolution framework， and deep unfolding network-based （DUN-based） spectral image super-resolution framework according to the network architecture. The E2EN-based spectral image super-resolution framework designs various network structures to mine nonlinear mapping relationships between low-resolution and high-resolution images. According to the basic computing unit of network structure， it can be divided into convolutional neural network-based method and Transformer-based method. The GAN-based spectral image super-resolution framework realizes the spectral image super-resolution through the game between the generator and the discriminator. The DUN-based spectral image super-resolution framework combines traditional optimization algorithms and deep learning， and it unfolds iterative optimization steps to form deep neural networks. From the perspective of supervision paradigm， the deep learning algorithms can also be classified into unsupervised and supervised categories. The supervised approaches minimize the distance between super-resolved spectral image
and ground truth， while unsupervised algorithms design loss function through the similarity between super-resolved and input images or through the game of the generator and the discriminator. Our critical review describes the main concepts and characteristics of each approach for different spectral image super-resolution tasks according to the network architecture and supervision paradigm. Second， we introduce the representative datasets and evaluation metrics. We divide the datasets into categories of single spectral image super-resolution datasets and fusion-based spectral image super-resolution datasets. Furthermore， the evaluation metrics can be grouped into full-reference metrics and no-reference metrics. Some full-reference metrics are widely used for the quantitative evaluation of spectral image super-resolution， including peak signal-to-noise， structural similarity， spectral angle mapper， and relative dimensionless global error in synthesis. Third， we provide the quantitative and qualitative experimental results of different spectral image super-resolution tasks. Finally， we summarize the challenges and problems in the study of deep learning-based spectral image super-resolution and conduct forecasting analysis， such as high-quality spectral image super-resolution dataset， model-driven and deep learning combined spectral image super-resolution method， real-time spectral image super-resolution， and comprehensive evaluation metrics. The methods and datasets mentioned are linked at
https://github.com/ColinTaoZhang/DL-based-spectral-super-resolution
https://github.com/ColinTaoZhang/DL-based-spectral-super-resolution
.
深度学习超分辨率光谱图像单图超分辨率融合超分辨率
deep learningsuper-resolutionspectral imagesingle image super-resolutionfusion-based super-resolution
Bandara W G C and Patel V M. 2022. Hypertransformer： a textural and spectral feature fusion transformer for pansharpening//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 1757-1767 ［DOI： 10.1109/CVPR52688.2022.00181http://dx.doi.org/10.1109/CVPR52688.2022.00181］
Bandara W G C， Valanarasu J M J and Patel V M. 2022. Hyperspectral pansharpening based on improved deep image prior and residual reconstruction. IEEE Transactions on Geoscience and Remote Sensing， 60： #5520816 ［DOI： 10.1109/TGRS.2021.3139292http://dx.doi.org/10.1109/TGRS.2021.3139292］
Bioucas-Dias J M， Plaza A， Dobigeon N， Parente M， Du Q， Gader P and Chanussot J. 2012. Hyperspectral unmixing overview： geometrical， statistical， and sparse regression-based approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 5（2）： 354-379 ［DOI： 10.1109/JSTARS.2012.2194696http://dx.doi.org/10.1109/JSTARS.2012.2194696］
Cai Y H， Lin J， Lin Z D， Wang H Q， Zhang Y L， Pfister H， Timofte R and Van Gool L. 2022. MST++： multi-stage spectral-wise transformer for efficient spectral reconstruction//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New Orleans， USA： IEEE： 744-754 ［DOI： 10.1109/CVPRW56347.2022.00090http://dx.doi.org/10.1109/CVPRW56347.2022.00090］
Cao X Y， Chen Y and Cao W F. 2022a. Proximal pannet： a model-based deep network for pansharpening//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 176-184 ［DOI： 10.1609/aaai.v36i1.19892http://dx.doi.org/10.1609/aaai.v36i1.19892］
Cao X Y， Fu X Y， Hong D F， Xu Z B and Meng D Y. 2022b. PanCSC-Net： a model-driven deep unfolding method for pansharpening. IEEE Transactions on Geoscience and Remote Sensing， 60： #5404713 ［DOI： 10.1109/TGRS.2021.3115501http://dx.doi.org/10.1109/TGRS.2021.3115501］
Chen L C， Papandreou G， Kokkinos I， Murphy K and Yuille A L. 2018. Deeplab： semantic image segmentation with deep convolutional nets， atrous convolution， and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence， 40（4）： 834-848 ［DOI： 10.1109/TPAMI.2017.2699184http://dx.doi.org/10.1109/TPAMI.2017.2699184］
Chen L W， Fu Y， You S D and Liu H Z. 2022. Hybrid supervised instance segmentation by learning label noise suppression. Neurocomputing， 496： 131-146 ［DOI： 10.1016/j.neucom.2022.05.026http://dx.doi.org/10.1016/j.neucom.2022.05.026］
Deng S Q， Deng L J， Wu X， Ran R， Hong D F and Vivone G. 2023. PSRT： pyramid shuffle-and-reshuffle transformer for multispectral and hyperspectral image fusion. IEEE Transactions on Geoscience and Remote Sensing， 61： #5503715 ［DOI： 10.1109/TGRS.2023.3244750http://dx.doi.org/10.1109/TGRS.2023.3244750］
Dian R W， Fang L Y and Li S T. 2017. Hyperspectral image super-resolution via non-local sparse tensor factorization//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 3862-3871 ［DOI： 10.1109/CVPR.2017.411http://dx.doi.org/10.1109/CVPR.2017.411］
Dian R W and Li S T. 2019. Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization. IEEE Transactions on Image Processing， 28（10）： 5135-5146 ［DOI： 10.1109/TIP.2019.2916734http://dx.doi.org/10.1109/TIP.2019.2916734］
Dong W Q， Qu J H， Zhang T Z， Li Y S and Du Q. 2022a. Context-aware guided attention based cross-feedback dense network for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 60： #5530814 ［DOI： 10.1109/TGRS.2022.3180484http://dx.doi.org/10.1109/TGRS.2022.3180484］
Dong W Q， Zhang T Z， Qu J H， Li Y S and Xia H M. 2022b. A spatial-spectral dual-optimization model-driven deep network for hyperspectral and multispectral image fusion. IEEE Transactions on Geoscience and Remote Sensing， 60： #5542016 ［DOI： 10.1109/TGRS.2022.3217542http://dx.doi.org/10.1109/TGRS.2022.3217542］
Dong W S， Zhou C， Wu F F， Wu J J， Shi G M and Li X. 2021. Model-guided deep hyperspectral image super-resolution. IEEE Transactions on Image Processing， 30： 5754-5768 ［DOI： 10.1109/TIP.2021.3078058http://dx.doi.org/10.1109/TIP.2021.3078058］
Dou X Y， Li C Y， Shi Q and Liu M X. 2020. Super-resolution for hyperspectral remote sensing images based on the 3D attention-SRGAN network. Remote Sensing， 12（7）： #1204 ［DOI： 10.3390/rs12071204http://dx.doi.org/10.3390/rs12071204］
Du D K， Gu Y F， Liu T Z and Li X. 2023. Spectral reconstruction from satellite multispectral imagery using convolution and transformer joint network. IEEE Transactions on Geoscience and Remote Sensing. 61： #5515015 ［DOI： 10.1109/TGRS.2023.3285893http://dx.doi.org/10.1109/TGRS.2023.3285893］
Fang S and Xu M. 2022. Hyperspectral and multispectral image fusion focused on error compensation. Journal of Image and Graphics， 28（1）： 277-289
方帅， 许漫. 2022. 面向误差补偿的高光谱与多光谱图像融合. 中国图象图形学报， 28（1）： 277-289 ［DOI： 10.11834/jig.220568http://dx.doi.org/10.11834/jig.220568］
Fu Y， Liang Z Y and You S D. 2021. Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 14： 2674-2688 ［DOI： 10.1109/JSTARS.2021.3057936http://dx.doi.org/10.1109/JSTARS.2021.3057936］
Fu Y， Zhang T， Wang L Z and Huang H. 2022a. Coded hyperspectral image reconstruction using deep external and internal learning. IEEE Transactions on Pattern Analysis and Machine Intelligence， 44（7）： 3404-3420 ［DOI： 10.1109/TPAMI.2021.3059911http://dx.doi.org/10.1109/TPAMI.2021.3059911］
Fu Y， Zhang T， Zheng Y Q， Zhang D B and Huang H. 2019. Hyperspectral image super-resolution with optimized RGB guidance//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 11653-11662 ［DOI： 10.1109/CVPR.2019.01193http://dx.doi.org/10.1109/CVPR.2019.01193］
Fu Y， Zhang T， Zheng Y Q， Zhang D B and Huang H. 2022b. Joint camera spectral response selection and hyperspectral image recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence， 44（1）： 256-272 ［DOI： 10.1109/TPAMI.2020.3009999http://dx.doi.org/10.1109/TPAMI.2020.3009999］
Fu Y， Zheng Y Q， Huang H， Sato I and Sato Y. 2018a. Hyperspectral image super-resolution with a mosaic RGB image. IEEE Transactions on Image Processing， 27（11）： 5539-5552 ［DOI： 10.1109/TIP.2018.2855412http://dx.doi.org/10.1109/TIP.2018.2855412］
Fu Y， Zheng Y R， Zhang L and Huang H. 2018b. Spectral reflectance recovery from a single RGB image. IEEE Transactions on Computational Imaging， 4（3）： 382-394 ［DOI： 10.1109/TCI.2018.2855445http://dx.doi.org/10.1109/TCI.2018.2855445］
Fu Y， Zheng Y R， Zhang L， Zheng Y Q and Huang H. 2020. Simultaneous hyperspectral image super-resolution and geometric alignment with a hybrid camera system. Neurocomputing， 384： 282-294 ［DOI： 10.1016/j.neucom.2019.12.024http://dx.doi.org/10.1016/j.neucom.2019.12.024］
Gao J H， Li J， Su X， Jiang M H and Yuan Q Q. 2022. Deep image interpolation： a unified unsupervised framework for pansharpening//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New Orleans， USA： IEEE： 608-617 ［DOI： 10.1109/CVPRW56347.2022.00076http://dx.doi.org/10.1109/CVPRW56347.2022.00076］
Guo Z L， Xin J W， Wang N N， Li J and Gao X B. 2022. External-internal attention for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 60： #5538714 ［DOI： 10.1109/TGRS.2022.3207230http://dx.doi.org/10.1109/TGRS.2022.3207230］
Han X L， Yu J and Sun W D. 2017. Hyperspectral image super-resolution based on non-factorization sparse representation and dictionary learning//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing， China： IEEE： 963-966 ［DOI： 10.1109/ICIP.2017.8296424http://dx.doi.org/10.1109/ICIP.2017.8296424］
Hang R L， Liu Q S and Li Z. 2021. Spectral super-resolution network guided by intrinsic properties of hyperspectral imagery. IEEE Transactions on Image Processing， 30： 7256-7265 ［DOI： 10.1109/TIP.2021.3104177http://dx.doi.org/10.1109/TIP.2021.3104177］
He J， Yuan Q Q， Li J and Zhang L P. 2022. PoNet： a universal physical optimization-based spectral super-resolution network for arbitrary multispectral images. Information Fusion， 80： 205-225 ［DOI： 10.1016/j.inffus.2021.10.016http://dx.doi.org/10.1016/j.inffus.2021.10.016］
He K M， Zhang X Y， Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 770-778 ［DOI： 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90］
Heylen R， Parente M and Gader P. 2014. A review of nonlinear hyperspectral unmixing methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 7（6）： 1844-1868 ［DOI： 10.1109/JSTARS.2014.2320576http://dx.doi.org/10.1109/JSTARS.2014.2320576］
Hohmann M， Kanawade R， Klämpfl F， Douplik A， Mudter J， Neurath M F and Albrecht H. 2017. In-vivo multispectral video endoscopy towards in-vivo hyperspectral video endoscopy. Journal of Biophotonics， 10（4）： 553-564 ［DOI： 10.1002/jbio.201600021http://dx.doi.org/10.1002/jbio.201600021］
Hu J， Jia X P， Li Y S， He G and Zhao M H. 2020. Hyperspectral image super-resolution via intrafusion network. IEEE Transactions on Geoscience and Remote Sensing， 58（10）： 7459-7471 ［DOI： 10.1109/tgrs.2020.2982940http://dx.doi.org/10.1109/tgrs.2020.2982940］
Hu J， Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 7132-7141 ［DOI： 10.1109/CVPR.2018.00745http://dx.doi.org/10.1109/CVPR.2018.00745］
Hu J， Zhao M H and Li Y S. 2019. Hyperspectral image super-resolution by deep spatial-spectral exploitation. Remote Sensing， 11（10）： #1229 ［DOI： 10.3390/rs11101229http://dx.doi.org/10.3390/rs11101229］
Hu J F， Huang T Z， Deng L J， Dou H X， Hong D F and Vivone G. 2022a. Fusformer： a transformer-based fusion network for hyperspectral image super-resolution. IEEE Geoscience and Remote Sensing Letters， 19： #6012305 ［DOI： 10.1109/LGRS.2022.3194257http://dx.doi.org/10.1109/LGRS.2022.3194257］
Hu J W， Liu Y T， Kang X D and Fan S S. 2022b. Multilevel progressive network with nonlocal channel attention for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 60： #5543714 ［DOI： 10.1109/TGRS.2022.322155http://dx.doi.org/10.1109/TGRS.2022.322155］
Huang G， Liu Z， Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 2261-2269 ［DOI： 10.1109/CVPR.2017.243http://dx.doi.org/10.1109/CVPR.2017.243］
Jiang J J， Sun H， Liu X M and Ma J Y. 2020. Learning spatial-spectral prior for super-resolution of hyperspectral imagery. IEEE Transactions on Computational Imaging， 6： 1082-1096 ［DOI： 10.1109/TCI.2020.2996075http://dx.doi.org/10.1109/TCI.2020.2996075］
Jiao J and Wu L D. 2019. Fusion of multispectral and panchromatic images via morphological filter and improved PCNN in NSST domain. Journal of Image and Graphics， 24（3）： 435-446
焦姣， 吴玲达. 2019. 形态学滤波和改进PCNN的NSST域多光谱与全色图像融合. 中国图象图形学报， 24（3）： 435-446 ［DOI： 10.11834/jig.180399http://dx.doi.org/10.11834/jig.180399］
Keshava N and Mustard J F. 2002. Spectral unmixing. IEEE Signal Processing Magazine， 19（1）： 44-57 ［DOI： 10.1109/79.974727http://dx.doi.org/10.1109/79.974727］
Lai Z Q， Wei K X and Fu Y. 2022. Deep plug-and-play prior for hyperspectral image restoration. Neurocomputing， 481： 281-293 ［DOI： 10.1016/j.neucom.2022.01.057http://dx.doi.org/10.1016/j.neucom.2022.01.057］
Li H L， Sudusinghe K， Liu Y Z， Yoon J， Van Der Schaar M， Blasch E and Bhattacharyya S S. 2017a. Dynamic， data-driven processing of multispectral video streams. IEEE Aerospace and Electronic Systems Magazine， 32（7）： 50-57 ［DOI： 10.1109/MAES.2017.160132http://dx.doi.org/10.1109/MAES.2017.160132］
Li J B， Li Y Q， Wang C， Ye X L and Heidrich W. 2023a. BUSIFusion： blind unsupervised single image fusion of hyperspectral and RGB images. IEEE Transactions on Computational Imaging， 9： 94-105 ［DOI： 10.1109/TCI.2023.3241549http://dx.doi.org/10.1109/TCI.2023.3241549］
Li J J， Cui R X， Li B， Song R， Li Y S and Du Q. 2019. Hyperspectral image super-resolution with 1D-2D attentional convolutional neural network. Remote Sensing， 11（23）： #2859 ［DOI： 10.3390/rs11232859http://dx.doi.org/10.3390/rs11232859］
Li J J， Du S C， Wu C X， Leng Y H， Song R and Li Y S. 2022a. DRCR Net： dense residual channel re-calibration network with non-local purification for spectral super resolution//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New Orleans， USA： IEEE： 1258-1267 ［DOI： 10.1109/CVPRW56347.2022.00132http://dx.doi.org/10.1109/CVPRW56347.2022.00132］
Li J J， Leng Y H， Song R， Liu W， Li Y S and Du Q. 2023b. MFormer： taming masked transformer for unsupervised spectral reconstruction. IEEE Transactions on Geoscience and Remote Sensing， 61： #5508412 ［DOI： 10.1109/TGRS.2023.3264976http://dx.doi.org/10.1109/TGRS.2023.3264976］
Li J J， Wu C X， Song R， Li Y S and Liu F. 2020b. Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle， USA： IEEE： 1894-1903 ［DOI： 10.1109/CVPRW50498.2020.00239http://dx.doi.org/10.1109/CVPRW50498.2020.00239］
Li J J， Wu C X， Song R， Li Y S， Xie W Y， He L H and Gao X B. 2023c. Deep hybrid 2D-3D CNN based on dual second-order attention with camera spectral sensitivity prior for spectral super-resolution. IEEE Transactions on Neural Networks and Learning Systems， 34（2）： 623-634 ［DOI： 10.1109/TNNLS.2021.3098767http://dx.doi.org/10.1109/TNNLS.2021.3098767］
Li Q， Wang Q and Li X L. 2020c. Mixed 2D/3D convolutional network for hyperspectral image super-resolution. Remote Sensing， 12（10）： #1660 ［DOI： 10.3390/rs12101660http://dx.doi.org/10.3390/rs12101660］
Li Q， Yuan Y， Jia X P and Wang Q. 2022a. Dual-stage approach toward hyperspectral image super-resolution. IEEE Transactions on Image Processing， 31： 7252-7263 ［DOI： 10.1109/TIP.2022.3221287http://dx.doi.org/10.1109/TIP.2022.3221287］
Li S T， Dian R W， Fang L Y and Bioucas-Dias J M. 2018. Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Transactions on Image Processing， 27（8）： 4118-4130 ［DOI： 10.1109/TIP.2018.2836307http://dx.doi.org/10.1109/TIP.2018.2836307］
Li X and Orchard M T. 2001. New edge-directed interpolation. IEEE Transactions on Image Processing， 10（10）： 1521-1527 ［DOI： 10.1109/83.951537http://dx.doi.org/10.1109/83.951537］
Li X， Wei H W and Zhang H Q. 2018. Super-resolution reconstruction of single remote sensing image combined with deep learning. Journal of Image and Graphics， 23（2）： 209-218
李欣， 韦宏卫， 张洪群. 2018. 结合深度学习的单幅遥感图像超分辨率重建. 中国图象图形学报， 23（2）： 209-218 ［DOI： 10.11834/jig.170194http://dx.doi.org/10.11834/jig.170194］
Li Y D， Mavromatis S， Zhang F， Du Z H， Sequeira J， Wang Z Y， Zhao X W and Liu R Y. 2022b. Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms. IEEE Transactions on Geoscience and Remote Sensing， 60： #3000224 ［DOI： 10.1109/TGRS.2021.3093043http://dx.doi.org/10.1109/TGRS.2021.3093043］
Li Y S， Hu J， Zhao X， Xie W Y and Li J J. 2017b. Hyperspectral image super-resolution using deep convolutional neural network. Neurocomputing， 266： 29-41 ［DOI： 10.1016/j.neucom.2017.05.024http://dx.doi.org/10.1016/j.neucom.2017.05.024］
Liang Z Y， Wang S， Zhang T and Fu Y. 2023. Blind super-resolution of single remotely sensed hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing， 61： #5523314 ［DOI： 10.1109/TGRS.2023.3302128http://dx.doi.org/10.1109/TGRS.2023.3302128］
Lin Y G， Zheng Y R， Fu Y and Huang H. 2018. Hyperspectral image super-resolution under misaligned hybrid camera system. IET Image Processing， 12（10）： 1824-1831 ［DOI： 10.1049/iet-ipr.2017.1340http://dx.doi.org/10.1049/iet-ipr.2017.1340］
Liu D H， Li J and Yuan Q Q. 2021. A spectral grouping and attention-driven residual dense network for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 59（9）： 7711-7725 ［DOI： 10.1109/tgrs.2021.3049875http://dx.doi.org/10.1109/tgrs.2021.3049875］
Liu J J， Wu Z B， Xiao L and Wu X J. 2022. Model inspired autoencoder for unsupervised hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 60： #5522412 ［DOI： 10.1109/TGRS.2022.3143156http://dx.doi.org/10.1109/TGRS.2022.3143156］
Liu W， Anguelov D， Erhan D， Szegedy C， Reed S， Fu C Y and Berg A C. 2016. SSD： single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 21-37 ［DOI： 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2］
Liu Z Y， Zhu H and Chen Z Z. 2023. Adversarial spectral super-resolution for multispectral imagery using spatial spectral feature attention module. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 16： 1550-1562 ［DOI： 10.1109/JSTARS.2023.3238853http://dx.doi.org/10.1109/JSTARS.2023.3238853］
Long Y Q， Wang X， Xu M， Zhang S Y， Jiang S G and Jia S. 2023. Dual self-attention swin transformer for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 61： #5512012 ［DOI： 10.1109/TGRS.2023.3275146http://dx.doi.org/10.1109/TGRS.2023.3275146］
Lore K G， Reddy K K， Giering M and Bernal E A. 2019. Generative adversarial networks for spectral super-resolution and bidirectional RGB-to-multispectral mapping//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 926-933 ［DOI： 10.1109/cvprw.2019.00122http://dx.doi.org/10.1109/cvprw.2019.00122］
Lu X C， Yang D Z， Zhang J P and Jia F D. 2021. Hyperspectral image super-resolution based on spatial correlation-regularized unmixing convolutional neural network. Remote Sensing， 13（20）： #4074 ［DOI： 10.3390/rs13204074http://dx.doi.org/10.3390/rs13204074］
Ma J Y， Yu W， Chen C， Liang P W， Guo X J and Jiang J J. 2020. Pan-GAN： an unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion， 62： 110-120 ［DOI： 10.1016/j.inffus.2020.04.006http://dx.doi.org/10.1016/j.inffus.2020.04.006］
Ma Q， Jiang J J， Liu X M and Ma J Y. 2022a. Deep unfolding network for spatiospectral image super-resolution. IEEE Transactions on Computational Imaging， 8： 28-40 ［DOI： 10.1109/TCI.2021.3136759http://dx.doi.org/10.1109/TCI.2021.3136759］
Ma Q， Jiang J J， Liu X M and Ma J Y. 2022b. Multi-task interaction learning for spatiospectral image super-resolution. IEEE Transactions on Image Processing， 31： 2950-2961 ［DOI： 10.1109/TIP.2022.3161834http://dx.doi.org/10.1109/TIP.2022.3161834］
Magid S A， Zhang Y L， Wei D L， Jang W D， Lin Z D， Fu Y and Pfister H. 2021. Dynamic high-pass filtering and multi-spectral attention for image super-resolution//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 4268-4277 ［DOI： 10.1109/ICCV48922.2021.00425http://dx.doi.org/10.1109/ICCV48922.2021.00425］
Masi G， Cozzolino D， Verdoliva L and Scarpa G. 2016. Pansharpening by convolutional neural networks. Remote Sensing， 8（7）： #594 ［DOI： 10.3390/rs8070594http://dx.doi.org/10.3390/rs8070594］
McElfresh C， Harrington T and Vecchio K S. 2018. Application of a novel new multispectral nanoparticle tracking technique. Measurement Science and Technology， 29（6）： #065002 ［DOI： 10.1088/1361-6501/aab940http://dx.doi.org/10.1088/1361-6501/aab940］
Mei S H， Jiang R T， Li X and Du Q. 2020. Spatial and spectral joint super-resolution using convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing， 58（7）： 4590-4603 ［DOI： 10.1109/TGRS.2020.2964288http://dx.doi.org/10.1109/TGRS.2020.2964288］
Mei S H， Yuan X， Ji J Y， Zhang Y F， Wan S and Du Q. 2017. Hyperspectral image spatial super-resolution via 3D full convolutional neural network. Remote Sensing， 9（11）： #1139 ［DOI： 10.3390/rs9111139http://dx.doi.org/10.3390/rs9111139］
Meng X C， Wang N， Shao F and Li S T. 2022. Vision transformer for pansharpening. IEEE Transactions on Geoscience and Remote Sensing， 60： #5409011 ［DOI： 10.1109/TGRS.2022.3168465http://dx.doi.org/10.1109/TGRS.2022.3168465］
Murguia J， Diaz G， Reeves T， Nelson R， Mooney J， Shepherd F， Griffith G and Franco D. 2010. Applications of multispectral video//Proceedings of SPIE 7780， Detectors and Imaging Devices： Infrared， Focal Plane， Single Photon. San Diego， USA： SPIE： #77800B ［DOI： 10.1117/12.861631http://dx.doi.org/10.1117/12.861631］
Nie J T， Zhang L， Wei W， Yan Q S， Ding C， Chen G C and Zhang Y N. 2023. A survey of hyperspectral image super-resolution method. Journal of Image and Graphics， 28（6）： 1685-1697
聂江涛， 张磊， 魏巍， 闫庆森， 丁晨， 陈国超， 张艳宁. 2023. 高光谱图像超分辨率重建技术研究进展. 中国图象图形学报， 28（6）： 1685-1697 ［DOI： 10.11834/jig.230038http://dx.doi.org/10.11834/jig.230038］
Qu J H， Dong W Q， Li Y S， Hou S X and Du Q. 2023. An interpretable unsupervised unrolling network for hyperspectral pansharpening. IEEE Transactions on Cybernetics， 53（12）： 7943-7956 ［DOI： 10.1109/TCYB.2023.3241165http://dx.doi.org/10.1109/TCYB.2023.3241165］
Qu Y， Qi H R and Kwan C. 2018. Unsupervised sparse dirichlet-net for hyperspectral image super-resolution//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 2511-2520 ［DOI： 10.1109/CVPR.2018.00266http://dx.doi.org/10.1109/CVPR.2018.00266］
Qu Y， Qi H R， Kwan C， Yokoya N and Chanussot J. 2022. Unsupervised and unregistered hyperspectral image super-resolution with mutual Dirichlet-Net. IEEE Transactions on Geoscience and Remote Sensing， 60： #5507018 ［DOI： 10.1109/tgrs.2021.3079518http://dx.doi.org/10.1109/tgrs.2021.3079518］
Ran R， Deng L J， Jiang T X， Hu J F， Chanussot J and Vivone G. 2023. GuidedNet： a general CNN fusion framework via high-resolution guidance for hyperspectral image super-resolution. IEEE Transactions on Cybernetics， 53（7）： 4148-4161 ［DOI： 10.1109/TCYB.2023.3238200http://dx.doi.org/10.1109/TCYB.2023.3238200］
Redmon J， Divvala S， Girshick R and Farhadi A. 2016. You only look once： unified， real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 779-788 ［DOI： 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91］
Ren S Q， He K M， Girshick R and Sun J. 2017. Faster R-CNN： towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（6）： 1137-1149 ［DOI： 10.1109/tpami.2016.2577031http://dx.doi.org/10.1109/tpami.2016.2577031］
Shelhamer E， Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（4）： 640-651 ［DOI： 10.1109/TPAMI.2016.2572683http://dx.doi.org/10.1109/TPAMI.2016.2572683］
Shi C and Wang L. 2014. Incorporating spatial information in spectral unmixing： a review. Remote Sensing of Environment， 149： 70-87 ［DOI： 10.1016/j.rse.2014.03.034http://dx.doi.org/10.1016/j.rse.2014.03.034］
Shi Y， Han L X， Han L H， Chang S， Hu T L and Dancey D. 2022. A latent encoder coupled generative adversarial network （LE-GAN） for efficient hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 60： #5534819 ［DOI： 10.1109/TGRS.2022.3193441http://dx.doi.org/10.1109/TGRS.2022.3193441］
Tang L F， Zhang H， Xu H and Ma J Y. 2023. Deep learning-based image fusion： a survey. Journal of Image and Graphics， 28（1）： 3-36
唐霖峰， 张浩， 徐涵， 马佳义. 2023. 基于深度学习的图像融合方法综述. 中国图象图形学报， 28（1）： 3-36 ［DOI： 10.11834/jig.220422http://dx.doi.org/10.11834/jig.220422］
Tang S Z， Xu Y， Huang L L and Sun L. 2019. Hyperspectral image super-resolution via adaptive dictionary learning and double ℓ1 constraint. Remote Sensing， 11（23）： #2809 ［DOI： 10.3390/rs11232809http://dx.doi.org/10.3390/rs11232809］
Wang Q， Li Q and Li X L. 2021a. Hyperspectral image superresolution using spectrum and feature context. IEEE Transactions on Industrial Electronics， 68（11）： 11276-11285 ［DOI： 10.1109/TIE.2020.3038096http://dx.doi.org/10.1109/TIE.2020.3038096］
Wang W， Zheng W H， Huang Y， Ding X H and Paisley J. 2019. Deep blind hyperspectral image fusion//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 4149-4158 ［DOI： 10.1109/ICCV.2019.00425http://dx.doi.org/10.1109/ICCV.2019.00425］
Wang X Y， Ma J Y and Jiang J J. 2022. Hyperspectral image super-resolution via recurrent feedback embedding and spatial-spectral consistency regularization. IEEE Transactions on Geoscience and Remote Sensing， 60： #5503113 ［DOI： 10.1109/TGRS.2021.3064450http://dx.doi.org/10.1109/TGRS.2021.3064450］
Wang Y， Chen X A， Han Z and He S Y. 2017. Hyperspectral image super-resolution via nonlocal low-rank tensor approximation and total variation regularization. Remote Sensing， 9（12）： #1286 ［DOI： 10.3390/rs9121286http://dx.doi.org/10.3390/rs9121286］
Wang Y D， Deng L J， Zhang T J and Wu X. 2021b. SSconv： explicit spectral-to-spatial convolution for pansharpening//Proceedings of the 29th ACM International Conference on Multimedia. Virtual Event： ACM： 4472-4480 ［DOI： 10.1145/3474085.3475600http://dx.doi.org/10.1145/3474085.3475600］
Wang Z and Bovik A. 2002. A universal image quality index. IEEE Signal Processing Letters， 9（3）： 81-84 ［DOI： 10.1109/97.995823http://dx.doi.org/10.1109/97.995823］
Wang Z J， Chen B， Lu R Y， Zhang H， Liu H W and Varshney P K. 2020. FusionNet： an unsupervised convolutional variational network for hyperspectral and multispectral image fusion. IEEE Transactions on Image Processing， 29： 7565-7577 ［DOI： 10.1109/TIP.2020.3004261http://dx.doi.org/10.1109/TIP.2020.3004261］
Wu C X， Li J J， Song R， Li Y S and Du Q. 2023. HPRN： holistic prior-embedded relation network for spectral super-resolution. IEEE Transactions on Neural Networks and Learning Systems： #3260828 ［DOI： 10.1109/TNNLS.2023.3260828http://dx.doi.org/10.1109/TNNLS.2023.3260828］
Xie Q， Zhou M H， Zhao Q， Meng D Y， Zuo W M and Xu Z B. 2019a. Multispectral and hyperspectral image fusion by MS/HS fusion net//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 1585-1594 ［DOI： 10.1109/cvpr.2019.00168http://dx.doi.org/10.1109/cvpr.2019.00168］
Xie Q， Zhou M H， Zhao Q， Xu Z B and Meng D Y. 2022. MHF-Net： an interpretable deep network for multispectral and hyperspectral image fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence， 44（3）： 1457-1473 ［DOI： 10.1109/TPAMI.2020.3015691http://dx.doi.org/10.1109/TPAMI.2020.3015691］
Xie W Y， Jia X P， Li Y S and Lei J. 2019b. Hyperspectral image super-resolution using deep feature matrix factorization. IEEE Transactions on Geoscience and Remote Sensing， 57（8）： 6055-6067 ［DOI： 10.1109/tgrs.2019.2904108http://dx.doi.org/10.1109/tgrs.2019.2904108］
Xiong Z H， Shi Z， Li H Q， Wang L Z， Liu D and Wu F. 2017. HSCNN： CNN-based hyperspectral image recovery from spectrally undersampled projections//Proceedings of 2017 IEEE International Conference on Computer Vision Workshops. Venice， Italy： IEEE： 518-525 ［DOI： 10.1109/ICCVW.2017.68http://dx.doi.org/10.1109/ICCVW.2017.68］
Xu Q Z， Li Y， Nie J Y， Liu Q J and Guo M Y. 2023. UPanGAN： unsupervised pansharpening based on the spectral and spatial loss constrained generative adversarial network. Information Fusion， 91： 31-46 ［DOI： 10.1016/j.inffus.2022.10.001http://dx.doi.org/10.1016/j.inffus.2022.10.001］
Xu S， Zhang J S， Zhao Z X， Sun K， Liu J M and Zhang C X. 2021. Deep gradient projection networks for pan-sharpening//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 1366-1375 ［DOI： 10.1109/CVPR46437.2021.00142http://dx.doi.org/10.1109/CVPR46437.2021.00142］
Xu X Y， Li J J and Hua Z. 2022. Transformer-based regression network for pansharpening remote sensing images. IEEE Transactions on Geoscience and Remote Sensing， 60： #5407423 ［DOI： 10.1109/TGRS.2022.3152425http://dx.doi.org/10.1109/TGRS.2022.3152425］
Xu Y， Wu Z B， Chanussot J and Wei Z H. 2020. Hyperspectral images super-resolution via learning high-order coupled tensor ring representation. IEEE Transactions on Neural Networks and Learning Systems， 31（11）： 4747-4760 ［DOI： 10.1109/TNNLS.2019.2957527http://dx.doi.org/10.1109/TNNLS.2019.2957527］
Yang G， Zhou M， Yan K Y， Liu A P， Fu X Y and Wang F. 2022. Memory-augmented deep conditional unfolding network for pansharpening//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 1778-1787 ［DOI： 10.1109/CVPR52688.2022.00183http://dx.doi.org/10.1109/CVPR52688.2022.00183］
Yang J F， Fu X Y， Hu Y W， Huang Y， Ding X H and Paisley J. 2017. PanNet： a deep network architecture for pan-sharpening//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 1753-1761 ［DOI： 10.1109/iccv.2017.193http://dx.doi.org/10.1109/iccv.2017.193］
Yang J X， Zhao Y Q， Chan J C W and Xiao L. 2019. A multi-scale wavelet 3D-CNN for hyperspectral image super-resolution. Remote Sensing， 11（13）： #1557 ［DOI： 10.3390/rs11131557http://dx.doi.org/10.3390/rs11131557］
Yao J， Hong D F， Chanussot J， Meng D Y， Zhu X X and Xu Z B. 2020. Cross-attention in coupled unmixing nets for unsupervised hyperspectral super-resolution//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 208-224 ［DOI： 10.1007/978-3-030-58526-6_13http://dx.doi.org/10.1007/978-3-030-58526-6_13］
Yuan Q Q， Wei Y C， Meng X C， Shen H F and Zhang L P. 2018. A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 11（3）： 978-989 ［DOI： 10.1109/JSTARS.2018.2794888http://dx.doi.org/10.1109/JSTARS.2018.2794888］
Zhang J Z， Xu T F， Jiang S W， Zhang Y H and Li J N. 2023a. Hyperspectral image joint super-resolution via implicit neural representation//Proceedings of SPIE 12565， Conference on Infrared， Millimeter， Terahertz Waves and Applications. Shanghai， China： SPIE： #125650Z ［DOI： 10.1117/12.2661749http://dx.doi.org/10.1117/12.2661749］
Zhang K W， Zhu D D， Min X K and Zhai G T. 2023b. Implicit neural representation learning for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing， 61： #5500212 ［DOI： 10.1109/TGRS.2022.3230204http://dx.doi.org/10.1109/TGRS.2022.3230204］
Zhang L， Lang Z Q， Wang P， Wei W， Liao S C， Shao L and Zhang Y N. 2020a. Pixel-aware deep function-mixture network for spectral super-resolution//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York， USA： AAAI： 12821-12828 ［DOI： 10.1609/aaai.v34i07.6978http://dx.doi.org/10.1609/aaai.v34i07.6978］
Zhang L， Nie J T， Wei W， Li Y and Zhang Y N. 2021. Deep blind hyperspectral image super-resolution. IEEE Transactions on Neural Networks and Learning Systems， 32（6）： 2388-2400 ［DOI： 10.1109/TNNLS.2020.3005234http://dx.doi.org/10.1109/TNNLS.2020.3005234］
Zhang L， Nie J T， Wei W， Zhang Y N， Liao S C and Shao L. 2020b. Unsupervised adaptation learning for hyperspectral imagery super-resolution//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 3070-3079 ［DOI： 10.1109/CVPR42600.2020.00314http://dx.doi.org/10.1109/CVPR42600.2020.00314］
Zhang L， Wei W， Bai C C， Gao Y F and Zhang Y N. 2018. Exploiting clustering manifold structure for hyperspectral imagery super-resolution. IEEE Transactions on Image Processing， 27（12）： 5969-5982 ［DOI： 10.1109/TIP.2018.2862629http://dx.doi.org/10.1109/TIP.2018.2862629］
Zhang L P， He J， Yang Q Q， Xiao Y and Yuan Q Q. 2022. Data-driven multi-source remote sensing data fusion： progress and challenges. Acta Geodaetica et Cartographica Sinica， 51（7）： 1317-1337
张良培， 何江， 杨倩倩， 肖屹， 袁强强. 2022. 数据驱动的多源遥感信息融合研究进展. 遥感学报. 51（7）： 1317-1337 ［DOI： 10.11947/j.AGCS.2022.20220171http://dx.doi.org/10.11947/j.AGCS.2022.20220171］
Zhang M J， Zhang C， Zhang Q M， Guo J， Gao X B and Zhang J. 2023c. ESSAformer： efficient transformer for hyperspectral image super-resolution//Proceedings of 2023 IEEE/CVF International Conference on Computer Vision. Paris， France： IEEE： 23016-23027 ［DOI： 10.1109/ICCV51070.2023.02109http://dx.doi.org/10.1109/ICCV51070.2023.02109］
Zhang T， Fu Y， Huang L W， Li S Y， You S D and Yan C G. 2023d. RGB-guided hyperspectral image super-resolution with deep progressive learning. CAAI Transactions on Intelligence Technology： #12256 ［DOI： 10.1049/cit2.12256http://dx.doi.org/10.1049/cit2.12256］
Zhang T， Fu Y and Li C. 2021. Hyperspectral image denoising with realistic data//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 2228-2237 ［DOI： 10.1109/ICCV48922.2021.00225http://dx.doi.org/10.1109/ICCV48922.2021.00225］
Zhang T， Fu Y and Li C. 2022a. Deep spatial adaptive network for real image demosaicing//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 3326-3334 ［DOI： 10.1609/aaai.v36i3.20242http://dx.doi.org/10.1609/aaai.v36i3.20242］
Zhang T， Fu Y， Wang L Z and Huang H. 2019. Hyperspectral image reconstruction using deep external and internal learning//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 8558-8567 ［DOI： 10.1109/ICCV.2019.00865http://dx.doi.org/10.1109/ICCV.2019.00865］
Zhang T， Fu Y， Zhang D B and Hu C. 2023e. Deep external and internal learning for noisy compressive sensing. Neurocomputing， 531： 61-73 ［DOI： 10.1016/j.neucom.2023.01.092http://dx.doi.org/10.1016/j.neucom.2023.01.092］
Zhang T， Fu Y and Zhang J. 2022b. Guided hyperspectral image denoising with realistic data. International Journal of Computer Vision， 130（11）： 2885-2901 ［DOI： 10.1007/s11263-022-01660-2http://dx.doi.org/10.1007/s11263-022-01660-2］
Zhang T， Fu Y， Zhang J and Yan C G. 2024. Deep guided attention network for joint denoising and demosaicing in real image. Chinese Journal of Electronics， 33（1）： 303-312 ［DOI： 10.23919/cje.2022.00.414http://dx.doi.org/10.23919/cje.2022.00.414］
Zhang T， Liang Z Y and Fu Y. 2022c. Joint spatial-spectral pattern optimization and hyperspectral image reconstruction. IEEE Journal of Selected Topics in Signal Processing， 16（4）： 636-648 ［DOI： 10.1109/JSTSP.2022.3179806http://dx.doi.org/10.1109/JSTSP.2022.3179806］
Zheng K， Gao L R， Hong D F， Zhang B and Chanussot J. 2022a. NonRegSRNet： a nonrigid registration hyperspectral super-resolution network. IEEE Transactions on Geoscience and Remote Sensing， 60： #5520216 ［DOI： 10.1109/TGRS.2021.3135501http://dx.doi.org/10.1109/TGRS.2021.3135501］
Zheng K， Gao L R， Liao W Z， Hong D F， Zhang B， Cui X M and Chanussot J. 2021b. Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution. IEEE Transactions on Geoscience and Remote Sensing， 59（3）： 2487-2502 ［DOI： 10.1109/TGRS.2020.3006534http://dx.doi.org/10.1109/TGRS.2020.3006534］
Zheng X T， Chen W J and Lu X Q. 2022b. Spectral super-resolution of multispectral images using spatial-spectral residual attention network. IEEE Transactions on Geoscience and Remote Sensing， 60： #5404114 ［DOI： 10.1109/TGRS.2021.3104476http://dx.doi.org/10.1109/TGRS.2021.3104476］
Zheng Y R， Zhang T and Fu Y. 2022c. A large-scale hyperspectral dataset for flower classification. Knowledge-Based Systems， 236： #107647 ［DOI： 10.1016/j.knosys.2021.107647http://dx.doi.org/10.1016/j.knosys.2021.107647］
Zheng Y X， Li J J， Li Y S， Guo J， Wu X Y and Chanussot J. 2020. Hyperspectral pansharpening using deep prior and dual attention residual network. IEEE Transactions on Geoscience and Remote Sensing， 58（11）： 8059-8076 ［DOI： 10.1109/TGRS.2020.2986313http://dx.doi.org/10.1109/TGRS.2020.2986313］
Zhou M， Huang J， Fang Y C， Fu X Y and Liu A P. 2022a. Pan-sharpening with customized transformer and invertible neural network//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 3553-3561 ［DOI： 10.1609/aaai.v36i3.20267http://dx.doi.org/10.1609/aaai.v36i3.20267］
Zhou M， Huang J， Li C Y， Yu H， Yan K Y， Zheng N S and Zhao F. 2022b. Adaptively learning low-high frequency information integration for pan-sharpening//Proceedings of the 30th ACM International Conference on Multimedia. Lisboa， Portugal： ACM： 3375-3384 ［DOI： 10.1145/3503161.3547924http://dx.doi.org/10.1145/3503161.3547924］
Zhou M， Huang J， Yan K Y， Yu H， Fu X Y， Liu A P， Wei X and Zhao F. 2022c. Spatial-frequency domain information integration for pan-sharpening//Proceedings of 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 274-291 ［DOI： 10.1007/978-3-031-19797-0_16http://dx.doi.org/10.1007/978-3-031-19797-0_16］
Zhou M， Yan K Y， Fu X Y， Liu A P and Xie C J. 2023. PAN-guided band-aware multi-spectral feature enhancement for pan-sharpening. IEEE Transactions on Computational Imaging， 9： 238-249 ［DOI： 10.1109/TCI.2023.3248956http://dx.doi.org/10.1109/TCI.2023.3248956］
Zhou M， Yan K Y， Huang J， Yang Z H， Fu X Y and Zhao F. 2022d. Mutual information-driven pan-sharpening//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 1788-1798 ［DOI： 10.1109/CVPR52688.2022.00184http://dx.doi.org/10.1109/CVPR52688.2022.00184］
Zhu Z Y， Hou J H， Chen J， Zeng H Q and Zhou J T. 2021a. Hyperspectral image super-resolution via deep progressive zero-centric residual learning. IEEE Transactions on Image Processing， 30： 1423-1438 ［DOI： 10.1109/tip.2020.3044214http://dx.doi.org/10.1109/tip.2020.3044214］
Zhu Z Y， Liu H， Hou J H， Jia S and Zhang Q F. 2021b. Deep amended gradient descent for efficient spectral reconstruction from single RGB images. IEEE Transactions on Computational Imaging， 7： 1176-1188 ［DOI： 10.1109/TCI.2021.3124364http://dx.doi.org/10.1109/TCI.2021.3124364］
Zhu Z Y， Liu H， Hou J H， Zeng H Q and Zhang Q F. 2021c. Semantic-embedded unsupervised spectral reconstruction from single RGB images in the wild//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 2259-2268 ［DOI： 10.1109/ICCV48922.2021.00228http://dx.doi.org/10.1109/ICCV48922.2021.00228］
相关文章
相关作者
相关机构
京公网安备11010802024621