Attention Enhanced Multi-patch Deformable Network for Image Deblurring
Jia Li, Xueming Li · 2020
End-to-end methods based on deep learning have gradually shown their convenience and advantages in non-uniform motion deblur. However, current deep models for deblurring still have some problems, which ignore the non-uniform deformation of motion blur kernel or global information, causing inadequately deblurring or artifacts. In this paper, we propose an enhanced network based on the four-layer multi-path hierarchy structure which divides image into multi-patches instead of down-sampling or other lossy way. Our network is combined with the self-attention module, and replace part of ordinary convolutions with deformable convolutions in the encoder-decoder backbone. For fewer artifacts and more deep feature details, perceptual loss is also applied in the training process. Compared with the results of prior frameworks, our method has improved nearly 0.4dB PSNR on the quantitative performance on GoPro testing data set, and also achieved clearer visual effect on several scenes.