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Received：14 August 2023，
Revised：2023-12-15，
Published：16 April 2024
移动端阅览
目的
2
图像逆半色调的目的是从二值半色调图像中恢复出连续色调图像。半色调图像丢失了大量原始图像内容信息，因此逆半色调成为一个经典的图像重建病态问题。现有的逆半色调算法重建效果无法满足对图像细节和纹理的需求。此外，已有方法大多忽略了训练策略对模型优化的重要影响，导致模型性能较差。针对上述问题，提出一个逆半色调网络以提高半色调图像重建质量。
方法
2
首先提出一个端到端的多尺度渐进式残差学习网络（multiscale progressivoly residual learning network， MSPRL）以恢复出更高质量的连续色调图像。该网络基于UNet架构并以多尺度图像作为输入；为充分利用不同尺度输入图像的信息，设计一个浅层特征提取模块以捕获多尺度图像的注意力信息；同时探讨不同学习策略对模型训练和性能的影响。
结果
2
实验在7个数据集上与6种方法进行对比。在Place365和Kodak数据集上，相比性能第2的方法，峰值信噪比（peak signal-to-noise ratio，PSNR）分别提高0.12 dB和0.18 dB；在其他5个常用于图像超分辨率的测试数据集Set5、Set14、BSD100（Berkeley segmentation dataset 100）、Urban100和Manga109上，相比性能第2的方法，PSNR值分别提高0.11 dB、0.25 dB、0.08 dB、0.39 dB和0.35 dB。基于本文的训练策略，重新训练的渐进式残差学习网络相比未优化训练模型在7个数据集上PSNR平均提高1.44 dB。本文方法在图像细节和纹理重建上实现最优效果。实验表明选用合适的学习策略能够优化模型训练，对性能提升具有重要帮助。
结论
2
本文提出的逆半色调模型，综合UNet架构和多尺度图像信息的优点，选用合适的训练策略，使得图像重建的细节与纹理更加清晰，视觉效果更加细致。本文算法代码公布在
https://github.com/Feiyuli-cs/MSPRL
https://github.com/Feiyuli-cs/MSPRL
。
Objective
2
The halftoning method represents continuous-tone images by using two levels of color， namely， black and white； it is commonly used in digital image printing， publishing， and displaying applications because of cost considerations. Compared with continuous-tone images， a halftone image has only two values. The halftoning method can save considerable storage space and network transfer bandwidth， so it is a feasible and important image compression method. Image inverse halftoning is a classic image restoration task， aiming to recover continuous-tone images from halftone images with only bilevel pixels. However， owing to the loss of original image content in halftone images， inverse halftoning is also a classic ill-problem. Although existing inverse halftoning algorithms have achieved good performance， their reconstruction results indicate lost image details and features， causing varying degrees of curvature and roughness in some high-frequency regions and resulting in poor visual reconstruction results， which still cannot meet the requirements for high detail and texture of images. Therefore， inverse halftoning remains a challenge in recovering high-quality continuous-tone images. Many previous methods focused on model design to improve performance， ignoring the important impact of training strategies on model optimization， which led to poor model performance. To solve these problems， we propose an inverse halftone network to improve the quality of halftone image reconstruction and explore different training strategies to optimize model training.
Method
2
In this paper， we propose an end-to-end multiscale progressively residual learning network （MSPRL）， which is based on the UNet architecture and takes multiscale input images. To make full use of different input image information， we design a shallow feature extraction module to capture the attention features of different-scale images. We divide our model into an encoder and a decoder， where the encoder focuses on restoring content information， and the decoder receives the aggregation features of the encoder to strengthen deep feature learning. The encoder and the decoder are composed of residual blocks （RBs）. We design our MSPRL to comprise three levels， each level receiving the input halftone images of different scales. To collect the encoder features and transmit them to the decoder， we use the
Concat
operation and a
<math id="M1"><mn mathvariant="normal">1</mn><mo>×</mo><mn mathvariant="normal">1</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051884&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051871&type=
7.11199999
2.28600001
convolutional kernel as the feature fusion module （FF） to aggregate the feature maps of different-level encoders. In our overall model， input halftone images are progressively learned from the left encoder to the right decoder. We systematically study the effects of different training strategies for model training and reconstruction performance. For example， the performance of using
<math id="M2"><mn mathvariant="normal">128</mn><mo>×</mo><mn mathvariant="normal">128</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051890&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051888&type=
13.37733269
2.28600001
pixel patch size is slightly lower than that of using
<math id="M3"><mn mathvariant="normal">256</mn><mo>×</mo><mn mathvariant="normal">256</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051882&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051892&type=
13.37733269
2.28600001
pixels patch size， but its training speed is significantly reduced by about 65% during the model training phase. Adding fast Fourier transform loss can further improve the model performance compared with the use of a single
<math id="M4"><msub><mrow><mi>L</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=57051913&type=
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2.70933342
3.21733332
loss. We also compare different feature channel dimensions， feature extraction blocks， and activation functions. Experimental results demonstrate that effective learning strategies can optimize model training and significantly improve performance.
Result
2
The experimental results are compared with the results of six methods on seven datasets， including a denoising convolutional neural network， VDSR， an enhanced deep super-resolution network， a progressively residual learning network （PRL）， a gradient-guided residual learning network， a multi-input multi-output UNet， and a retrained PRL （PRL-dt）. On the Places365 and Kodak datasets， compared with that of the second-best-performing model PRL-dt， the peak signal-to-noise ratio （PSNR） of our MSPRL is increased by 0.12 dB and 0.18 dB， respectively. On the other five commonly used test datasets （Set5， Set14， BSD100， Urban100， and Manga109） for image super-resolution， compared with that of the second-best-performing model PRL-dt， the PSNR of MSPRL is increased by 0.11 dB， 0.25 dB， 0.08 dB， 0.39 dB and 0.35 dB， respectively. Based on our training strategies， PRL-dt has an average PSNR improvement of 1.44 dB compared with the unoptimized training PRL on the seven test datasets. Extensive experiments demonstrate that MSPRL achieves significant reconstruction results in image details and textures.
Conclusion
2
In this paper， we propose an inverse halftone network to solve the problem of low-quality reconstruction for inverse halftoning. Our MSPRL contains an SFE， an FF， and an encoder and a decoder with RBs as the core. It combines the advantages of the UNet architecture and multiscale image information and chooses appropriate training strategies to improve image reconstruction quality and the visual effects in terms of details and textures. Extensive experiments demonstrate that our MSPRL outperforms previous approaches and achieves state-of-the-art performance.
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