VCR: Variance-aware Channel Recalibration Network for Low Light Image with Distribution Alignment

Zhixin Cheng, Haodian Wang, Liangsheng Liu, Wenfei Yang, Baoqun Yin

Keywords: Channel Recalibration, Distribution Alignment, Image Enhancement

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-aware Channel Recalibration Network for Low Light Image with Distribution Alignment (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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 1474