Abstract: To address the problem that existing deblurring methods cannot effectively restore fine details of images, an image deblurring method combining a U-shaped network and Transformer is proposed. Firstly, a multi-scale feature extraction module is used to extract shallow feature information from the image. Then, a hierarchical nested U-shaped subnet with a stepwise feature enhancement module is employed to obtain deep feature information while preserving image detail information. Next, a local-global residual refinement module is constructed, which fully extracts global and local information through information interaction between convolutional neural networks and Swin Transformer, and further refines the feature information. Finally, a 1×1 convolutional layer is used for feature reconstruction. The proposed method achieves a peak signal-to-noise ratio (PSNR) of 32.92 and a structural similarity index mean (SSIM) of 0.964 on the GoPro dataset, both outperforming other comparative methods. Experimental results demonstrate that the proposed method can effectively remove blur and reconstruct a potentially clear image with rich details.

Key words: image deblurring, detailed information, hierarchical nested U-shaped subnet, Transformer, multi-scale feature