Image Enhancement Algorithm Based on GAN Neural Network

Abstract

Deep underwater color images have problems such as low brightness, poor contrast, and loss of local details. In order to effectively enhance low-quality underwater images, this paper proposes an enhancement method based on GAN (Generative Adversarial Network). This paper studies low-light image enhancement algorithms, aiming to improve the quality of low-light images by studying some technical means and methods, and restore the original scene information of low-quality images, so as to obtain natural and clear images with complete details and structural information. In order to verify the effectiveness of this method, image databases such as DIARETDB0 and SID are used as the research object, combined with multi-scale Retinex color reproduction contrast-constrained adaptive histogram equalization to compare the performance of the enhanced algorithm. The results show that the processed image is better than other image enhancement methods in terms of color protection, contrast enhancement, and image detail enhancement. The proposed method significantly improves the indicators proposed in the article.