现代电子技术2024，Vol.47Issue(15)：53-59,7.DOI:10.16652/j.issn.1004-373x.2024.15.009
基于生成对抗网络的弱光图像增强方法
Low-light image enhancement method based on generative adversarial network
摘要
Abstract
Most of the existing low-light image enhancement methods suffer from issues such as color distortion,poor denoising performance,and heavy reliance on paired training datasets.To overcome these challenges,a low-light image enhancement method based on generative adversarial network is proposed.The proposed model comprises two parts,named a generator and a discriminator.In the former part,the improved UNet network with integrated EMA(efficient multi-scale attention)is used to enhance the image,while in the later part,the multi-branch discriminator including color discriminator,grayscale discriminator and multi-scale discriminator is used to fuse and judge the authenticity of the image.The experimental results show that the proposed method achieves excellent results on public datasets,and shows significant improvements in evaluation indexes such as PSNR(peak signal-to-noise ratio),SSIM(structural similarity index measure),NIQE(natural image quality evaluator)and BRISQUE(blind/referenceless image spatial quality evaluator),which further proves the effectiveness and robustness of the proposed method.关键词
弱光图像/无监督学习/生成器/判别器/注意力机制/图像增强Key words
low-light image/unsupervised learning/generator/discriminator/attention mechanism/image enhancement分类
电子信息工程引用本文复制引用
武霁,丁冰,丁洁..基于生成对抗网络的弱光图像增强方法[J].现代电子技术,2024,47(15):53-59,7.基金项目
国家重点研发计划(2022YFB3204600) （2022YFB3204600）
北京理工大学青年教师学术启动计划 （）
