AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring
Authors: Xintian Mao, Qingli Li, Yan Wang

Despite the recent progress in enhancing the efficacy of image deblurring the limited decoding capability constrains the upper limit of State-Of-The-Art (SOTA) methods. This paper proposes a pioneering work Adaptive Patch Exiting Reversible Decoder (AdaRevD) to explore their insufficient decoding capability. By inheriting the weights of the well-trained encoder we refactor a reversible decoder which scales up the single-decoder training to multi-decoder training while remaining GPU memory-friendly. Meanwhile we show that our reversible structure gradually disentangles high-level degradation degree and low-level blur pattern (residual of the blur image and its sharp counterpart) from compact degradation representation. Besides due to the spatially-variant motion blur kernels different blur patches have various deblurring difficulties. We further introduce a classifier to learn the degradation degree of image patches enabling them to exit at different sub-decoders for speedup. Experiments show that our AdaRevD pushes the limit of image deblurring e.g. achieving 34.60 dB in PSNR on GoPro dataset.