Luminance Component Analysis for Exposure Correction

TL;DR

This paper introduces Luminance Component Analysis (LCA), a novel method inspired by PCA, that effectively separates luminance-related features from other image information for improved exposure correction, reducing distortions and preserving details.

Contribution

LCA applies an orthogonal constraint within a U-Net structure and employs geometric optimization to decouple luminance features, enabling more accurate exposure correction.

Findings

LCA achieves the highest PSNR and SSIM scores in experiments.

LCA runs at 28.72 FPS, demonstrating efficiency.

Decoupling luminance features reduces color distortions.

Abstract

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.