Image dehazing method via a cycle generative adversarial network

Abstract

Aiming at the problems of colour distortion and residual dehazing in the existing image dehazing methods when processing outdoor images, a method of image dehazing based on the cycle generative adversarial network is proposed. Taking the cycle generative adversarial network as the overall framework of the model, firstly, the neural network is trained to obtain the mapping relationship between haze images and haze-free images. Secondly, to accelerate the convergence speed of network training, using residual structure to improve network stability and reduce parameters. Then, a new loss function is proposed, fusing the Wasserstein distance into adversarial loss and cycle consistency loss, which reduces the deviation between the generated dehaze image and the real haze-free image, and alleviating the problems of colour distortion and haze removal residue. Finally, use the optimised bounded ReLU (BReLU) activation function instead of the original activation function to improve the transmittance reflecting the haze depth information. The experimental results demonstrated that, compared with the comparison method, the proposed method improves the peak signal-to-noise ratio, structure similarity, information entropy, and average gradient, and has an achieved better performance in dehazing.