CDCNet: A Fast and Lightweight Dehazing Network with Color Distortion Correction

Mobile edge devices require real-time dehazing methods that sustain dehazing performance while drastically reducing resource occupation. However, color distortion is a substantial challenge for lightweight dehazing networks, which profoundly impairs image quality. In this paper, we propose CDCNet, a fast and lightweight dehazing network with color distortion correction, to tackle these challenges. Firstly, CDCNet utilizes a multi-scale feature aggregation module to retain lightweight and leverages a parallel attention module to expedite the dehazing process. Secondly, we devise proportional residual connection and loss functions to mitigate potential color distortion in CDCNet. Thirdly, we design a post-processing module to adjust HSV and Lab color spaces to eliminate color distortions comprehensively. Experiments demonstrate that CDCNet surpasses state-ofthe-art lightweight dehazing methods and exhibits superiority in execution time.