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.
Abstract (translated)
大多数基于sRGB的低光图像增强（LLIE）方法都存在亮度和色彩相互纠缠的问题，而HSV颜色空间虽然在一定程度上可以分离这两者，但会引入显著的红色和黑色噪声。最近提出了HVI颜色空间来解决这些问题，通过色度极化和强度压缩提高色彩保真度。然而，现有方法可能会因亮度通道与色度通道之间的一致性问题而受到影响，导致不自然的增强效果。为了解决这些挑战，我们提出了一种新的低光图像增强框架——基于方差驱动通道校准的稳健低光增强（VCR）。该框架包含两个主要组件：通道自适应调整（CAA）模块和颜色分布对齐（CDA）模块。CAA模块采用方差引导的特征过滤来提升模型在高亮度区域与色彩分布上的关注；而CDA模块则确保颜色特征空间中的分布一致性。这些设计提高了低光条件下的感知质量。实验结果表明，在多个基准数据集上，所提出的方法相较于现有方法达到了最先进的性能水平。
URL
https://arxiv.org/abs/2603.10975
