Progressive deep network for blind motion image deblurring

Abstract

The multi-stage deep neural network in the blind motion image deblurring task lacks a large range of receptive fields and it was difficult to reasonably interact with the image features of each stage. A progressive depth network (PDNet) with dilated convolution and contextual attention fusion module was thus proposed for restoring sharp images. This method included three stages:local feature extraction, image feature integration and image restoration. In the local feature extraction stage and image feature integration stage, multi-branch dilated convolution module (MDCB) was used to increase receptive field to adapt to different degrees of motion blur. In the image feature integration stage and image restoration stage, the contextual attention fusion module was used for information interaction of image features in different stages to achieve progressive image feature enhancement. The proposed method could make full use of local and global image features to guide image restoration through the three-stage progressive enhancement strategy, so as to generate sharp and high-quality images. The experimental results showed that compared with SRN and other networks, the proposed PDNet had a better effect on GoPro data set and RealBlur-J data set, and the peak signal to noise ratio (PSNR) had an average increase of 2.9 dB, and the structural similarity (SSIM) had an average increase of 0.05.

Keywords

• blind motion image deblurring
• contextual attention fustion
• progressive deep network
• dilated convolution