Low-light image enhancement using generative adversarial networks

• Published in: Scientific reports Vol. 14; no. 1; pp. 18489 - 14
• Main Authors: Wang, Litian, Zhao, Liquan, Zhong, Tie, Wu, Chunming
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 09.08.2024; Nature Portfolio
• Subjects: Brightness; Color; Generative adversarial networks; Illumination; Illumination attention module; Image processing; Information processing; Light; Methods; Multi-scale feature extraction module; Nighttime road scene image enhancement; Generative adversarial networks; Multi-scale feature extraction module; Nighttime road scene image enhancement; Illumination attention module
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69505-1

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.