Abstract
Deblurring, denoising and super-resolution (SR) are important image recovery tasks that are committed to improving image quality. Despite the rapid development of deep learning and vast studies on improving image quality have been proposed, the most existing recovery solutions simply deal with quality degradation caused by a single distortion factor, such as SR focusing on improving spatial resolution. Since very little work has been done to analyze the interaction and characteristics of the deblurring, denoising and SR mixing problems, this paper considers the multi-distortion image recovery problem from a holistic perspective and introduces an end-to-end multi-stream fusion network (MSFN) to restore a multi-distortion image (low-resolution image with noise and blur) into a clear high-resolution (HR) image. Firstly, MSFN adopts multiple reconstruction branches to extract deblurring, denoise and SR features with respect to different degradations. Then, MSFN gradually fuses these multi-stream recovery features in a determined order and obtains an enhanced restoration feature by using two fusion modules. In addition, MSFN uses fusion modules and residual attention modules to facilitate the fusion of different recovery features from the denoising branch and the deblurring branch for the trunk SR branch. Experiments on several benchmarks fully demonstrate the superiority of our MSFN in solving the multi-distortion image recovery problem.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62077037 and 61872241, in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0102, in part by the Science and Technology Commission of Shanghai Municipality under Grants 18410750700 and 17411952600, in part by Shanghai Lin-Gang Area Smart Manufacturing Special Project under Grant ZN2018020202-3, and in part by Project of Shanghai Municipal Health Commission(2018ZHYL0230).
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Wen, Y. et al. (2021). Multi-Stream Fusion Network for Multi-Distortion Image Super-Resolution. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_19
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