Inner crossover fusion network with pixel-wise sampling for dynamic scene deblurring

Abstract

Despite dynamic scene deblurring methods based on multi-scale or scale-recursive or hierarchical multi-patch strategy have made great progress, they still have some shortcomings. In the multi-scale or scale-recursive networks, bicubic down-sampling operation may cause some important high frequency information loss, e.g., strong edges. In addition, these networks are limited to using the same receptive field information while neglecting the cross-scale and cross-receptive field relationships inside the subnetwork, which may lead to information drop-out. To alleviate the above problems, we propose an inner crossover fusion network with pixel-wise sampling, called ICFNet, for dynamic scene deblurring. More specifically, we first design a pixel-wise sampling operation to replace bicubic down-sampling in the multi-scale or scale-recursive networks, so as to avoid the loss of the strong edge information. In addition, different from multi-scale and multi-patch architecture, we design a novel inner crossover fusion architecture to make full use of blur information with cross-scale and cross-receptive field in the network. To maximize the advantages of the ICFNet architecture, we design three region-enhanced residual blocks with different receptive fields for learning multiple receptive field features. Finally, extensive experimental results on different datasets have demonstrated that the proposed network can obtain better deblurring effect.