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Image restoration is a low-level vision task, most CNN methods are designedas a black box, lacking transparency and internal aesthetics. Although somemethods combining traditional optimization algorithms with DNNs have beenproposed, they all have some limitations. In this paper, we propose athree-granularity memory layer and contrast learning named MemoryNet,specifically, dividing the samples into positive, negative, and actual threesamples for contrastive learning, where the memory layer is able to preservethe deep features of the image and the contrastive learning converges thelearned features to balance. Experiments on Derain/Deshadow/Deblur taskdemonstrate that these methods are effective in improving restorationperformance. In addition, this paper's model obtains significant PSNR, SSIMgain on three datasets with different degradation types, which is a strongproof that the recovered images are perceptually realistic. The source code ofMemoryNet can be obtained from https://github.com/zhangbaijin/MemoryNet

Memory augment is All You Need for image restoration