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.
Abstract (translated)
图像分解为视觉数据的成像因素提供了深刻见解，并显著增强了各种先进的计算机视觉任务。在这项工作中，我们介绍了一种基于分解物理信息先验的新方法来改进低光条件下的图像增强技术。现有直接将低光图像映射到正常光照条件下图像的方法在sRGB色彩空间中存在颜色预测不一致以及对光谱功率分布（SPD）变化高度敏感的问题，导致其在不同照明条件下表现不稳定。为解决这些问题，我们引入了物理信息感知的颜色变换框架——Physics-informed Color-aware Transform (PiCat)，它通过我们提出的Color-aware Transform (CAT) 将低光图像从sRGB色彩空间转换成深度的光照不变描述符。这种变换能够更好地处理复杂的光照和SPD变化问题，从而增强其鲁棒性。 此外，我们提出了内容噪声分解网络（CNDN），该网络通过减少噪声和其他失真来优化描述符分布，使低光图像的内容表示与良好照明条件下的表示更加一致，进而有效地恢复了这些内容的表示。CAT和CNDN共同作为物理先验知识，在从低光照域到正常光照领域的转换过程中提供指导。 我们的PiCat框架在五个基准数据集上展示出优于现有最佳方法的表现。
URL
https://arxiv.org/abs/2504.11896
