提出VCR框架，提升暗光图像增强的色彩一致性与自然度。
VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement
- 基于方差引导的通道重校准，聚焦高亮与色彩分布区域。
- 通过颜色分布对齐模块，解决亮度与色度不一致问题。
- 适用于需要真实感增强的低光照图像处理场景。
基于sRGB的低光照图像增强方法普遍存在亮度与色彩耦合问题，而HSV色彩空间虽能解耦但引入明显红黑噪声。近期提出的HVI色彩空间通过色度极化和亮度压缩提升了色彩保真度，但仍存在亮度与色度通道层面的不一致，导致色彩分布错位，产生不自然的增强结果。为此，本文提出稳健的低光照增强框架VCR（Variance-Driven Channel Recalibration）。该框架包含两个核心模块：通道自适应调节（CAA）模块，利用方差引导的特征滤波，强化模型对高亮度与丰富色彩区域的关注；颜色分布对齐（CDA）模块，在色彩特征空间强制分布对齐。实验在多个基准数据集上验证了该方法优于现有技术，显著提升低光条件下的感知质量。
原文摘要 · Abstract (English)
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.
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