Downhole Image Enhancement Algorithm Based on Improved CycleGAN

Due to the complex environment of underground coal mines, the quality of captured images is often poor. The dim light in the underground leads to low image contrast and loss of detailed features; meanwhile, the dust and water vapor in the underground makes the image less clear and unevenly bright and dark. In addition, due to the small number of relevant datasets in the underground, it is unable to meet the requirements of deep learning algorithms, which causes great trouble to the underground image enhancement. To address the above problems, this paper proposes an image enhancement algorithm based on CycleGAN network. To address the problem of difficulty in acquiring paired image data in downhole, CycleGAN network is used for label-free training to expand the dataset; to address the problem of loss of image details in downhole, the improved UNET network DCSAU-Net is used instead of the original ResNet network to better preserve the image detail features and structure; by introducing self-attention in the discriminator of CycleGAN mechanism in the discriminator of CycleGAN to further improve the attention and reconstruction ability of important image information; finally, the EM distance with penalty term is used to replace the JS distance to alleviate the difficult problem that the CycleGAN network is not easy to converge. The experimental results show that compared with the original CycleGAN, the algorithm in this paper improves $6.281 \%$, $15.714 \%, 7.627 \%$, and $15.670 \%$ in the four indexes of PSNR, SSIM, IFC, and VIF, respectively. In terms of the visual effect of the images, the improved CycleGAN network generates images with higher clarity and better quality, which proves that the algorithm in this paper can effectively realize the low illumination image enhancement of coal mine underground.