Multi-scale Image Blind Deblurring Network for Dynamic Scenes

Recently, the convolutional neural network (CNN) based single-image dynamic scene blind deblurring (SIDSBD) methods have made significant progress. Their success mainly stems from the multi-scale/multi-patch model and the design of the encoder-decoder architecture and the residual block structure. In this paper, a novel multi-scale CNN (MSCNN) is proposed to further exploit the advantages of the multi-scale model, the encoder-decoder architecture, and the residual block structure, which can achieve higher-quality SIDSBD. First, inspired by the spatial pyramid pooling (SPP) and the multi-patch model, this study put forward a hierarchical multi-patch channel attention (HMPCA) strategy to perform adaptive weight assignment for feature images channel-wise by using the global and local feature statistics. The proposed HMPCA uses local information, which can be considered to enlarge the receptive field in the channel direction and thus can enhance the representational ability of the network. Then, different from existing multi-scale models, a novel multi-scale model is built, in which each scale consists of multiple encoders and decoders. Because of the HMPCA, the encoders and decoders at the same scale are not exactly the same. The proposed multi-scale model can be regarded to increase the depth of the encoder-decoder architecture, thus able to improve the deblurring performance of each scale and finally achieve higher-quality blind deblurring for dynamic scenes. Extensive experiments comparing the proposed SIDSBD method with state-of-the-art ones demonstrate the superiority of the method in terms of both qualitative evaluation and quantitative metrics. © 2022 Chinese Academy of Sciences. All rights reserved.