An attention mechanism and GAN based low-light image enhancement method

Abstract

The low-light image has degradation problems such as low contrast, noise artifact, loss of details and color distortion, which cause poor visual perception quality of the image, and reduce the accuracy of subsequent image recognition, classification, and detection tasks. To solve the above problems, a low-light image enhancement method is proposed, which combines attention mechanism and Generative Adversarial Networks (GAN). A generator network model with convolutional attention module (CBAM) and residual block is designed. Three loss functions of content, color and texture are proposed for image features. The experiment is compared with the four advanced methods on LOL dataset. In this dataset, the experimental results are at least 5% better than those of other methods. The experimental results show that the saliency model proposed in this paper can effectively enhance the low-light image and obtain a good visual effect.