Image blind motion deblurring method with longitudinal channel and wavelet dynamic convolution

Abstract

Deep learning methods have achieved great success in image deblurring. However, these methods also exist two limitations, the detailed information loss caused by the image encoding, and the poor robustness caused by fixed weight parameters. To address the two issues, we propose an image deblurring method based on DeblurGAN, which is good at removing motion blurs. Firstly, two structures are specially designed, respectively. On one hand, the longitudinal channel network structure is proposed. It can directly fuse the original image features into each feature layer of the encoder, so as to retain more detailed information of the original image. On the other hand, the wavelet dynamic convolution module is applied to the feature extraction process. It can generate dynamic wavelet kernels according to different input images rather than fixed convolution parameters. In addition, we use the improved activation function and loss function to retrain the network. The quantitative experimental results show that the PSNR and SSIM metrics of our method can reach 29.9605 and 0.9177 on the GoPro test set, respectively. The qualitative experimental results indicate that our method can retain the original image details well, and achieve promising robustness.