Multi-Stage Motion Blur Image Restoration Network Based on Transformer
Motion blur is a common cause of image degradation that limits image readability and subsequent processing.A multi-stage deblurring network based on the Transformer is proposed to address the limited receptive field of convolutional networks and information loss in conventional multi-stage networks.The network adopts a multi-stage encoder-decoder structure with skip connections within and between stages to enhance information propagation.First,an efficient Transformer module is used to process the global and local information of the image using channel attention and depthwise convolution.Second,a multi-branch feedforward network with multiple parallel branches is introduced to extract and fuse features at different scales and levels.Finally,superior image restoration results are achieved through multi-stage residual learning.Experimental results show that the proposed method achieves a Peak Signal-to-Noise Ratio(PSNR)of 32.23 dB and Structural Similarity Index Measure(SSIM)of 0.955 on the GoPro dataset,and a PSNR of 30.15 dB and SSIM of 0.930 on the HIDE dataset,demonstrating a performance superior to DeepDeblur,DeblurGAN-V2,and other models.
deep learningTransformer modelattention mechanismimage restorationmulti-scale network
万方数据
维普
