A Self-Attention CycleGAN for Unsupervised Image Hazing

The high cost and difficulty of collecting real-world foggy scene images mean that automatic driving datasets produce limited images in bad weather and lead to deficient training in automatic driving systems, causing unsafe judgments and leading to traffic accidents. Therefore, to effectively promote the safety and robustness of an autonomous driving system, we improved the CycleGAN model to achieve dataset augmentation of foggy images. Firstly, by combining the self-attention mechanism and the residual network architecture, the sense of hierarchy of the fog effect in the synthesized image was significantly refined. Then, LPIPS was employed to adjust the calculation method for cycle consistency loss to make the synthetic picture more similar to the original one in terms of perception. The experimental results showed that the FID index of the foggy image generated by the improved CycleGAN network was reduced by 3.34, the IS index increased by 15.8%, and the SSIM index increased by 0.1%. The modified method enhances the generation of foggy images, while retaining more details of the original image and reducing content distortion.