Luminance Component Analysis for Exposure Correction
This addresses exposure correction for image processing by improving separation of luminance components, though it is incremental as it builds on existing methods like PCA and U-Net.
The paper tackled the problem of exposure correction by proposing Luminance Component Analysis (LCA), which decouples luminance-related and luminance-unrelated features using an orthogonal constraint in a U-Net structure, achieving a PSNR of 21.33 and SSIM of 0.88 with 28.72 FPS on a dataset.
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.