Abstract: Aiming at  the problems of texture blur and poor image quality in the image processed by single image blind deblurring  algorithm, we  proposed an image blind deblurring network with embedded  attention mechanism. Firstly, the network adopted multi-scale cyclic architecture, which realized multi-scale by cyclic processing of images with different resolutions inside the network, and restored images from coarse to fine. Secondly, a residual channel selection module and jump connection were embedded in the network  to enhance feature extraction, and a multi-scale  structural loss function was designed for training optimization. Finally, it was  compared with the classical  deblurring networks on two widely used datasets GoPro and Kohler, and a visual comparison was made on Lai dataset. The experimental results show that the proposed network achieves a good visual effect of image blind deblurring,  compared with other algorithms, it improves the peak signal-to-noise ratio and structural similarity, and can improve the poor quality of image deblurring.

Key words: image processing, blind deblurring, attention mechanism, multi-scale network