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

2026-03-10

AI Summary

This work addresses the common issue of distortion in low-light image enhancement caused by inconsistencies between luminance and chrominance channels and misaligned color distributions. To mitigate these problems, the authors propose a variance-driven channel recalibration framework that operates in the HVI color space. By introducing variance-guided feature filtering and integrating a Channel Adaptive Adjustment (CAA) module with a Color Distribution Alignment (CDA) module, the method effectively decouples luminance and chrominance information, suppresses noise-induced artifacts, and enhances color fidelity. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-art methods across multiple benchmark datasets, achieving superior performance in both visual quality and perceptual naturalness.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Privacy-enhancing technologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• low-light image enhancement
• channel inconsistency
• color distribution alignment
• luminance-chrominance decoupling

Innovation

• Variance-Driven Channel Recalibration
• Low-Light Image Enhancement
• Color Distribution Alignment
• Channel Adaptive Adjustment
• HVI Color Space