Dust image restoration based on channel difference prior and depth residual attention network

Abstract

Images captured in dust weather have severe yellow and local red, and traditional color correction methods have low robustness. Thus, a dust image restoration method based on channel difference prior (CDP) and a depth residual attention network is proposed in this paper. First, CDP based on statistical observations is defined and used to acquire a dust image dataset. Then, a deep residual network that combines channel-spatial attention is introduced, and channel attention and spatial attention are used to describe important information for every residual block feature. Finally, channel fusion and nonlinear outputs are used to generate clear images. Rich experiments have confirmed the effectiveness and rationality of the proposed method.