A feature attention based multi-stage network for image deblurring

Abstract

A feature attention based multi-stage network for image deblurring is proposed. The feature attention module is introduced into the model. This module is composed of channel attention and pixel attention mechanism. More attention is focused on the blurred pixels and important channel information, solving the problem of uneven blurred distribution in images effectively. We also introduce the atrous residual block and the context module between the encoder and the decoder. The atrous convolution are combined with the residual, and the context module adopts the multi-layer atrous convolution, which effectively increase the receptive field of the network and better capture the multi-scale contextual information. Experiments were conducted on the public dataset GoPro to evaluate the performance of our method. The results show that the PSNR of the proposed model reaches 30.51, and the processing speed reaches 0.035s, which outperform that of the most current deblurring methods.