DFC-dehaze: an improved cycle-consistent generative adversarial network for unpaired image dehazing

Recently, cycle-consistent adversarial network (CycleGAN) has been utilized in image dehazing tasks due to train without paired images. However, the quality of generated dehazed images is low. To enhance the dehazed images, this paper presents an improved CycleGAN model, called DFC-dehaze, in which the convolutional neural network (CNN)-based generator is replaced by the Dehazeformer-t model. To reduce the haze residuals in the recovered images, the local-global discriminator is used to handle locally varying haze. When the generated images are blurred or have unrealistic colors, the discriminator gives them the low scores by the negative sample punishment mechanism. Another, the structural similarity index measurement (SSIM) loss is combined with the cyclic consistent loss to improve the visual quality of the dehazed images. The experimental results demonstrate that the proposed methods outperform other unsupervised methods and yield visual outcomes that are comparable to those produced by supervised methods.