Non-uniform Deblurring by Deep Sharpness Edge Guided Model

Hao Wei, Chenyang Ge, Xin Qiao, Pengchao Deng

Abstract: In this paper, we propose a two-branch deblurring framework. Given a blurred image, we first extract the edge map and employ an edge refinement network to recover the structure. Then the refined edge map is utilized to guide the subsequent deblurring process for correct structure recovery. Specifically, we develop a lightweight omni-dimensional attention module for long-range dependencies modeling and plug it into the edge refinement network, which effectively handles blur patterns with high variation. Furthermore, we propose a dynamic feature upsample module, which integrates dynamic convolution with upsampling and adaptively deals with the non-uniform blur. Extensive experiments show that our method outperforms state-of-the-art methods.