Blind Motion Deblurring Using Multi-scale Residual Channel Attention Network

In recent years, multi-scale approach has been applied to image restoration tasks, including super-resolution, deblurring, etc., and has been proved beneficial to both optimization-based methods and learning-based methods to improve the restoration performance. Meanwhile, it is observed that high-frequency information plays an important role in blind motion deblurring. Unlike previous learning-based methods, which simply deepen deblurring network without discriminating the low-frequency contents and the high-frequency details, we propose a novel multi-scale convolutional neural network (CNN) framework with residual channel attention block (RCAB) for blind motion deblurring. RCAB has the residual in residual (RIR) structure, which consists of several residual groups with long skip connections and allows low-frequency information pass through the skip connections conveniently, and can adaptively learn more useful channel-wise features and pay more attention to high-frequency information. Experimental results show that our proposed method can obtain better deblurring images than state-of-the-art learning-based image deblurring methods in terms of both quantitative metrics and visual quality.