Attentive deep network for blind motion deblurring on dynamic scenes

Abstract

Non-uniform blind motion deblurring is a challenging yet important problem in image processing that receives enduring attention in the last decade. The non-uniformity nature of motion blurring leads to great variations on the blurring effects across image regions and over different images, which makes it very difficult to train an end-to-end deblurring neural network (NN) with good generalization performance. This paper introduces an attention mechanism for the blind deblurring NN, including both spatial and channel attention, so as to effectively handle the significant spatial variations on blurring effects. In the attention mechanism, the spatial attention is introduced in both the encoder for discriminative exploitation of image edges and smooth regions and the decoder for discriminative treatment on different regions with different blurring effects. The channel attention is introduced for better generalization performance of the NN, as it allows adaptive weighting on intermediate features for a particular image. Building such an attention mechanism into a multi-scale encoder–decoder framework, an attentive NN is developed for practical non-uniform blind image deblurring. The experiments on several benchmark datasets show that the proposed NN can effectively restore the images degraded by spatially-varying blurring, with state-of-the-art performance.