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 luminance-unrelated 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 luminance-unrelated 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.

翻译：曝光校正方法旨在调整亮度同时保持其他与亮度无关的信息。然而，当前曝光校正方法难以完全分离亮度相关与亮度无关分量，导致色彩失真、细节丢失，并需要额外的修复步骤。受主成分分析（PCA）启发，本文提出一种称为亮度分量分析（LCA）的曝光校正方法。LCA在U-Net结构中应用正交约束，以解耦亮度相关与亮度无关特征。通过解耦的亮度相关特征，LCA仅调整亮度相关分量，同时保持亮度无关分量不变。为优化正交约束问题，LCA采用几何优化算法，将欧氏空间中的约束问题转换为正交Stiefel流形上的无约束问题。大量实验表明，LCA能够从RGB色彩空间中解耦亮度特征。此外，LCA在曝光校正数据集中取得了最佳的PSNR（21.33）和SSIM（0.88）指标，处理速度为28.72 FPS。