Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2504.11896v1
• Date: Wed, 16 Apr 2025 09:23:38 GMT
• Title: Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement
• Authors: Xingxing Yang, Jie Chen, Zaifeng Yang,
• Abstract summary: We introduce a novel approach to low-light image enhancement based on decomposed physics-informed priors.<n>Existing methods that directly map low-light to normal-light images in the sRGB color space suffer from inconsistent color predictions.<n>Our proposed PiCat framework demonstrates superior performance compared to state-of-the-art methods across five benchmark datasets.
• Score: 5.8550460201927725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image decomposition offers deep insights into the imaging factors of visual data and significantly enhances various advanced computer vision tasks. In this work, we introduce a novel approach to low-light image enhancement based on decomposed physics-informed priors. Existing methods that directly map low-light to normal-light images in the sRGB color space suffer from inconsistent color predictions and high sensitivity to spectral power distribution (SPD) variations, resulting in unstable performance under diverse lighting conditions. To address these challenges, we introduce a Physics-informed Color-aware Transform (PiCat), a learning-based framework that converts low-light images from the sRGB color space into deep illumination-invariant descriptors via our proposed Color-aware Transform (CAT). This transformation enables robust handling of complex lighting and SPD variations. Complementing this, we propose the Content-Noise Decomposition Network (CNDN), which refines the descriptor distributions to better align with well-lit conditions by mitigating noise and other distortions, thereby effectively restoring content representations to low-light images. The CAT and the CNDN collectively act as a physical prior, guiding the transformation process from low-light to normal-light domains. Our proposed PiCat framework demonstrates superior performance compared to state-of-the-art methods across five benchmark datasets.

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