Research on Multi-Scale Feature Fusion Dehazing Network Based on Feature Differences

Abstract

Haze, formed by the accumulation and concentration of atmospheric pollutants under meteorological conditions, such as temperature inversion, severely limits visibility. Image dehazing techniques aim to eliminate issues caused by haze, such as image blur and low contrast, thereby enhancing image clarity and visibility. However, challenges persist regarding the loss of image details. To address this issue, a feature difference-based multi-scale feature fusion dehazing network known as FD-CA dehaze is proposed in this study. In this network, the basic block structure of the FFA-Net is enhanced by extracting intermediate feature information from the feature difference, coordinate, and channel dimensions. An Effective Coordinate Attention (ECA) module that combines global pooling, max pooling, and coordinate positional information is introduced. This module mitigates the positional information loss during feature fusion. By integrating channel attention with the ECA module, a Dual Attention (D-CA) model that enables better utilization of spatial and channel information is constructed. Consequently, the model exhibits enhanced performance in image dehazing tasks. Furthermore, the loss function is improved by combining L1 loss function with perceptual loss. Experimental results on the Synthetical Objective Test Set (SOTS) and Hybrid Subjective Test Set (HSTS) demonstrate that the FD-CA dehaze network achieves a Peak Signal-to-Noise Ratio (PSNR) of 37.93 dB and a Structural Similarity Index (SSIM) of 0.990 5. Experimental results demonstrate that compared to classic dehazing networks such as FFA-Net and GridDehazeNet, FD-CA dehaze achieves significant improvement and better dehazing performance.