CP-Net: Channel Attention and Pixel Attention Network for Single Image Dehazing

Abstract

An end-to-end channel attention and pixel attention network (CP-Net) is proposed to produce dehazed image directly in the paper. The CP-Net structure contains three critical components. Firstly, the double attention (DA) module consisting of channel attention (CA) and pixel attention (PA). Different channel features contain different levels of important information, and CA can give more weight to relevant information, so the network can learn more useful information. Meanwhile, haze is unevenly distributed on different pixels, and PA is able to filter out haze with varying weights for different pixels. It sums the outputs of the two attention modules to improve further feature representation which contributes to better dehazing result. Secondly, local residual learning and DA module constitute another important component, namely basic block structure. Local residual learning can transfer the feature information in the shallow part of the network to the deep part of the network through multiple local residual connections and enhance the expressive ability of CP-Net. Thirdly, CP-Net mainly uses its core component, DA module, to automatically assign different weights to different features to achieve satisfactory dehazing effect. The experiment results on synthetic datasets and real hazy images indicate that many state-of-the-art single image dehazing methods have been surpassed by the CP-Net both quantitatively and qualitatively.