VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement

TL;DR

This paper introduces VCR, a novel low-light image enhancement framework that uses variance-driven channel recalibration to improve color fidelity and perceptual quality, outperforming existing methods on benchmark datasets.

Contribution

The paper proposes VCR, featuring CAA and CDA modules, to address luminance-color entanglement and channel inconsistency in low-light enhancement.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively enhances perceptual quality under low-light conditions.

Improves color fidelity and reduces artifacts.

Abstract

Most sRGB-based LLIE methods suffer from entangled luminance and color, while the HSV color space offers insufficient decoupling at the cost of introducing significant red and black noise artifacts. Recently, the HVI color space has been proposed to address these limitations by enhancing color fidelity through chrominance polarization and intensity compression. However, existing methods could suffer from channel-level inconsistency between luminance and chrominance, and misaligned color distribution may lead to unnatural enhancement results. To address these challenges, we propose the Variance-Driven Channel Recalibration for Robust Low-Light Enhancement (VCR), a novel framework for low-light image enhancement. VCR consists of two main components, including the Channel Adaptive Adjustment (CAA) module, which employs variance-guided feature filtering to enhance the model's focus on regions with high intensity and color distribution. And the Color Distribution Alignment (CDA) module, which enforces distribution alignment in the color feature space. These designs enhance perceptual quality under low-light conditions. Experimental results on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods.