文献类型：期刊文献

英文题名：Luminance Component Analysis for Exposure Correction

作者：Peng, Jingchao[1,2]; Bashford-Rogers, Thomas[2]; Chen, Jingkun[3]; Zhao, Haitao[1]; Hu, Zhengwei[4]; Debattista, Kurt[3]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, 200237, China; [2] University of Warwick, WMG, United Kingdom; [3] University of Oxford, Department of Engineering Science, Institute of Biomedical Engineering, Oxford, United Kingdom; [4] College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

年份：2026

外文期刊名：IEEE Transactions on Artificial Intelligence

收录：EI(收录号：20260720089506)

语种：英文

外文关键词：Color - Constrained optimization - Geometry - Luminance

摘要：Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in fully separating luminance-related and luminanceunrelated components, leading to distortions in color, loss of detail, and requiring extra restoration procedures. Inspired by principal component analysis (PCA), this paper proposes an exposure correction method called luminance component analysis (LCA). LCA applies the orthogonal constraint to a U-Net structure to decouple luminance-related and luminanceunrelated features. With decoupled luminance-related features, LCA adjusts only the luminance-related components while keeping the luminance-unrelated components unchanged. To optimize the orthogonal constraint problem, LCA employs a geometric optimization algorithm, which converts the constrained problem in Euclidean space to an unconstrained problem in orthogonal Stiefel manifolds. Extensive experiments show that LCA can decouple the luminance feature from the RGB color space. Moreover, LCA achieves the best PSNR (21.33) and SSIM (0.88) in the exposure correction dataset with 28.72 FPS. ? 2020 IEEE.