This study proposes an image enhancement method combining conditional entropy and generative adversarial network, aiming to improve the image quality while avoiding overfitting through the negative training of dependent generative adversarial network and introducing dependent entropy distance loss. Through NIQMC, NIQE and BTMQI evaluation indexes, this paper evaluates the effects of different parameter combinations and image chunk sizes on the enhancement results. It utilizes information entropy as an evaluation index to measure the impact of conditional entropy distance loss. The effectiveness of adversarial learning and conditional entropy in image enhancement is verified by comparing the experimental results. The experiments show that the system can achieve the best image quality of SSIM=0.9852, PSNR=27.58, and SNROI=21.34 with the parameters S=50 and R=4.0%, indicating that the method can effectively retain the detailed information and realism of the Image while enhancing the clarity of the Image, demonstrating a significant performance advantage.