BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement

2026-08-22

AI Summary

本文提出SA-RF和BC-IHV方法，通过分离色度/强度分支、条件金字塔及HybridAda解决低光图像增强中的曝光校正与结构保持问题。

Abstract

Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport can model exposure ambiguity; however, its flexibility may also alter observable geometry and chromatic content. Moreover, fixed invertible color coordinates are usually treated only as representations, although their inverse mappings reshape the RGB-domain gradients received by the enhancement network. To address these issues, we propose Structure-Anchored Rectified Flow (SA-RF), which maintains correspondence through separate chromaticity/intensity stems, a scale-matched condition pyramid, and HybridAda. HybridAda assigns location-specific retrieval to spatial cross-attention and global exposure modulation to pooled AdaLN. We further introduce BC-IHV, a learnable Box--Cox polar color space whose analytically invertible intensity mapping controls the inverse-gradient dynamic range through a single exponent. This allows the representation to balance dark-range expansion and gradient conditioning instead of adopting a fixed linear or logarithmic law. Experiments on three LOL benchmarks, blind image-quality evaluation, and cross-dataset tests demonstrate consistent reconstruction and perceptual advantages over the sota. Controlled studies further support the effectiveness of both the proposed framework and color representation.

Problem

• Low-light image enhancement
• exposure ambiguity
• generative transport
• color space
• gradient conditioning

Innovation

• Structure-Anchored Rectified Flow
• HybridAda
• BC-IHV
• Box-Cox polar color space