Single image dehazing based on Multi-scale Channel Attention Mechanism

Abstract

Haze is a natural phenomenon that can cause discomfort to the visual system. Since the essential information of hazy images, i.e., the channel features of the images, has been neglected in most previous studies, it leads to the degradation of the generalization ability of the model. In this paper, we propose a multi-scale image dehazing neural network model based on the channel attention mechanism. In this paper, this model consists mainly of a network structure consisting of multi-scale encoding-decoding module, channel attention mechanism module, and multi-scale residual module. We demonstrate the robustness of the proposed dehazing network model in this paper by conducting qualitative as well as quantitative analysis on synthetic datasets and real-world datasets.