提出LCA方法分离亮度与颜色信息，提升曝光矫正效果。
Luminance Component Analysis for Exposure Correction
- 基于PCA思想设计U-Net结构，通过正交约束解耦亮度相关与无关特征。
- 在曝光矫正数据集上达21.33分PSNR和0.88分SSIM，推理速度28.72 FPS。
- 适合需要精准色彩保持的图像修复与摄影后期场景使用。
曝光矫正方法旨在调整亮度的同时保留其他与亮度无关的信息。然而，现有方法难以完全分离亮度相关与无关成分，导致颜色失真、细节丢失，且需额外修复步骤。受主成分分析（PCA）启发，本文提出亮度成分分析（LCA）方法。LCA在U-Net结构中引入正交约束，以解耦亮度相关与无关特征。利用解耦后的亮度相关特征，仅调整其成分，保持其余成分不变。为优化正交约束问题，LCA采用几何优化算法，将欧几里得空间中的约束问题转化为正交斯特雷尔流形上的无约束问题。大量实验表明，LCA能有效从RGB色彩空间中分离亮度特征。此外，其在曝光矫正数据集上达到最佳性能：PSNR为21.33，SSIM为0.88，推理速度达28.72 FPS。
原文摘要 · Abstract (English)
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.
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