计算机工程2024，Vol.50Issue(2)：247-255,9.DOI:10.19678/j.issn.1000-3428.0067252
融合门控变换机制和GAN的低光照图像增强方法
Low-light Image Enhancement Method Combining Gated Transformation Mechanism and GAN
摘要
Abstract
To address the problems of paired image data dependence,serious detail loss,and noise amplification in low-light image enhancement,a low-light image enhancement method that combines a gated channel transformation mechanism with Generative Adversarial Network(GAN)is proposed.This method can be trained without low/normal-light image pairs.First,a feature extraction network composed of multi-scale convolution residual modules and a gated channel transformation convolution and residual module is designed.The Gated Channel Transformation(GCT)unit extracts global context features and multi-scale local feature information of the input images.In the feature fusion network,the convolution residual structure is used to fully fuse the extracted deep and shallow features,and a horizontal jump connection structure is introduced to retain the detailed feature information to the maximum extent in obtaining the final enhanced image.Finally,the joint loss function is introduced to guide the network training process,restraining image noise and enhancing image color more naturally and symmetrically.The experimental results show that this method can effectively improve the brightness and contrast of the low-light image and reduce image noise,generating a clearer enhanced image and a more realistic color.In practice,average Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),and Natural Image Quality Evaluator(NIQE)reaches 16.48 dB,0.93,3.37,respectively.Compared with other algorithms,the proposed method provides significant advantages in subjective visual analysis and objective index evaluation.关键词
低光照图像增强/卷积残差结构/门控通道变换单元/无监督学习/生成对抗网络Key words
low-light image enhancement/convolution residual structure/Gated Channel Transformation(GCT)unit/unsupervised learning/Generative Adversarial Network(GAN)分类
信息技术与安全科学引用本文复制引用
何银银,胡静,陈志泊,张荣国..融合门控变换机制和GAN的低光照图像增强方法[J].计算机工程,2024,50(2):247-255,9.基金项目
国家自然科学基金(32071775) （32071775）
博士科研启动基金(20202057) （20202057）
山西省自然科学基金(202203021211206,202203021211189). （202203021211206,202203021211189）
