现代电影技术Issue(1)：25-34,10.DOI:10.3969/j.issn.1673-3215.2026.01.003
基于物理引导的图像亮度增强神经网络研究
Research on physics-guided image luminance enhancement neural network
摘要
Abstract
To address the challenges of insufficient brightness and the difficulty of simultaneously correcting noise and color deviation in low-light images,this paper proposes a physics-guided image luminance enhancement neural network.The proposed method decomposes the image into luminance and chrominance components within a linear space,and forms a physical backbone with a monotonic tone curve controlled by a few hyperparameters,while a residual network learns only amplitude-limited local compensation.Furthermore,multiple physical constraints are introduced as loss functions.Experi-mental results on a low-light street scene dataset demonstrate that the model achieves superior performance.The re-search confirms that combining interpretable physical models with data-driven models significantly improves the natural-ness and stability of the enhancement results.Meanwhile,it provides a technical foundation for cross-scenario applications.关键词
神经网络/低照度/图像增强/影视画面增强/交互画面增强Key words
Neural Network/Low-Light/Image Enhancement/Video Vision Enhancement/Interactive Vision Enhancement分类
信息技术与安全科学引用本文复制引用
杨璨,鄢凯杰,陈晓悦,刘一苇..基于物理引导的图像亮度增强神经网络研究[J].现代电影技术,2026,(1):25-34,10.基金项目
2025年度国家社科基金艺术学年度项目"智能影像创作与传播的中国路径与自主体系研究"(25AC006). （25AC006）
