西安电子科技大学学报（自然科学版）2025，Vol.52Issue(5)：1-12,12.DOI:10.19665/j.issn1001-2400.20250601
联合颜色融合与特征增强的低光图像去雾网络
Jointcolor fusion and feature enhancement network for low-light image dehazing
摘要
Abstract
Existing dehazing networks exhibit a limited capability in feature extraction and color bias suppression under low-light haze conditions,often leading to detail loss and color distortion.To address these issues,we propose FCformer,a Joint Feature Enhancement and Color Fusion Network for low-light image dehazing.To restore the image structure and texture,a feature enhancement backbone is designed with window-spatial and sparse-channel modules to focus on key local and global features.A color fusion branch,by incorporating color correction and fusion,improves chromatic representation.A learnable prior constraint module based on atmospheric scattering and Retinex models regularizes the output.Finally,a composite loss function,by combining reconstruction,perceptual,and color losses,guides better detail and color restoration.Experiments show that the FCformer surpasses the DehazeFormer by 0.98 dB in PSNR with a similar parameter size,and achieves a PSNR comparable to that of the ACANet while reducing parameters by 96.84％,demonstrating a superior visual performance.关键词
低光图像去雾/特征增强/色彩融合/注意力机制/深度学习Key words
low-light image dehazing/feature enhancement/color fusion/attention mechanism/deep learning分类
信息技术与安全科学引用本文复制引用
王柯俨,宗星芃,成吉聪,董鑫宇,李云松..联合颜色融合与特征增强的低光图像去雾网络[J].西安电子科技大学学报（自然科学版）,2025,52(5):1-12,12.基金项目
陕西省自然科学基础研究计划(2025JC-YBMS-728,2024JC-YBQN-0635) （2025JC-YBMS-728,2024JC-YBQN-0635）
国家自然科学基金(62121001) （62121001）
中央高校基本科研业务费专项(XJSJ23087,QTZX25003) （XJSJ23087,QTZX25003）
