End-to-end XY Separation for Single Image Blind Deblurring

Single image blind deblurring, only exploiting a blurry observation to reconstruct the sharp image, is a popular yet challenging low-level vision task. Current state-of-the-art deblurring networks mainly follow the coarse-to-fine strategy for architecture design and utilize U-net or its variant, XYDeblur, as the basic units. However, the one-encoder-one-decoder and the recently proposed one-encoder-two-decoder structures of basic units both fail to comprehensively take advantage of the directional separability of 2D deblurring, which increases the learning content of networks, thus leading to performance degradation. To thoroughly decouple the deblurring into two spatially orthogonal parts, we propose a novel substitution for U-net and its variant, called XYU-net. Specifically, it consists of two structurally identical U-nets, named XU-net and YU-net. They share orthogonal parameters by rotating kernels and focus on restoring a 2D blurry image in two spatially orthogonal directions respectively, which not only brings efficiency enhancement but also maintains parameter number. To further reduce the graphics memory demand of XYU-net, we transfer some non-linear transform modules (NLTM) from the outside of the network to its inside and propose the modified version, called MXYU-net. Experimental results on three large blurry image datasets demonstrate the efficiency of XYU-net and MXYU-net compared with U-net and XYDeblur, both as standalone models and as basic units of advanced U-net-based deblurring networks.