DR-UNet: dynamic residual U-Net for blind correction of optical degradation

Jingwen Zhou; Shiqi Chen; Qi Li; Tongyue Li; Huajun Feng

doi:10.1117/12.2662187

12 April 2023 DR-UNet: dynamic residual U-Net for blind correction of optical degradation

Jingwen Zhou, Shiqi Chen, Qi Li, Tongyue Li, Huajun Feng

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125651B (2023) https://doi.org/10.1117/12.2662187
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

Due to the size limitation of mobile devices, their optical design is difficult to reach the level of professional equipment. Corresponding restoration methods are then needed to compensate for the shortage. However, most of the models are still static, which leads to their limited representation ability of images. To tackle this problem, we propose a plug-and-play deformable residual block for efficiently sampling the spatially related features at different scales. Moreover, considering that the optical degradation is closely correlated with the field-of-view (FOV), we introduce a FOV attention block based on omni-dimensional dynamic convolution to integrate spatial features. On this basis, we further propose a novel optical degradation correction model called DR-UNet. It is constructed on an encoder-decoder structure to capture multiscale information, along with several context blocks. By correcting the optical degradation in images from coarse to fine, we finally obtain high-quality and degradation-free images. Extensive results demonstrate that our method can compete favorably with some state-of-the-art methods.

Citation Download Citation

Jingwen Zhou, Shiqi Chen, Qi Li, Tongyue Li, and Huajun Feng "DR-UNet: dynamic residual U-Net for blind correction of optical degradation", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125651B (12 April 2023); https://doi.org/10.1117/12.2662187

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