西安工程大学学报2025，Vol.39Issue(2)：84-92,9.DOI:10.13338/j.issn.1674-649x.2025.02.010
改进RetinexNet的低光照图像增强方法
Low-light image enhancement method based on improved RetinexNet
摘要
Abstract
Under low-light conditions,images often suffer from loss of details and increased noise,which seriously affects the image quality.Therefore,this paper proposes a low-light image enhancement method incorporating an attention mechanism.Base on RetinexNet model,first,in the decomposition network,more feature information and details were retained by introducing a feature fusion module,which connected the shallow features horizontally and input them into the deep network.Secondly,to address the problem of high noise in the reflection component,the NAFNet noise removal module was added to the noise reduction network,which effectively re-duced the impact of noise on image quality.Finally,in the luminance enhancement network,the Unet structure was adopted and the channel attention(CA)was embedded,which enabled it to learn the correlation between different feature channels and the feature representation of a specific channel under different lighting conditions,thus significantly improving the enhancement effect of the illumination map.The experimental results show that compared with RetinexNet,the method in this paper has significant improvement in various indexes.Specifically,the peak signal-to-noise ratio is improved by about 1.05 dB,the average absolute difference is improved by about 0.03,the structural similarity is improved by about 0.09,the image similarity is improved by about 0.05,and the natural image quality is improved by about 0.75.In summary,the method in this paper can effectively suppress noise and significantly improve the enhancement of low-light image de-tails.关键词
RetinexNet/特征融合/低光照图像增强/卷积神经网络/通道注意力机制Key words
RetinexNet/feature fusion/low-light image enhancement/convolutional neural net-work/channel attention分类
信息技术与安全科学引用本文复制引用
顾梅花,丁梦玥,董晓晓..改进RetinexNet的低光照图像增强方法[J].西安工程大学学报,2025,39(2):84-92,9.基金项目
陕西省科技厅面上项目(2024JC-YBMS-491) （2024JC-YBMS-491）
