Abstract
Low-light image denoising and enhancement are challenging, especially when traditional noise assumptions, such as Gaussian noise, do not hold in majority. In many real-world scenarios, such as low-light imaging, noise is signal-dependent and is better represented as Poisson noise. In this work, we address the problem of denoising images degraded by Poisson noise under extreme low-light conditions. We introduce a light-weight deep learning-based method that integrates Retinex based decomposition with Poisson denoising into a unified encoder-decoder network. The model simultaneously enhances illumination and suppresses noise by incorporating a Poisson denoising loss to address signal-dependent noise. Without prior requirement for reflectance and illumination, the network learns an effective decomposition process while ensuring consistent reflectance and smooth illumination without causing any form of color distortion. The experimental results demonstrate the effectiveness and practicality of the proposed low-light illumination enhancement method. Our method significantly improves visibility and brightness in low-light conditions, while preserving image structure and color constancy under ambient illumination.
Abstract (translated)
在低光照条件下对图像进行去噪和增强是一项挑战，特别是在传统噪声假设（如高斯噪声）不成立的情况下。实际上，在许多场景中，例如低光成像时，噪声是与信号相关的，并且更适合作为泊松噪声来表示。在这项工作中，我们针对极端低光环境下受到泊松噪声污染的图像去噪问题提出了解决方案。我们引入了一种基于轻量级深度学习的方法，该方法将Retinex分解技术与泊松去噪相结合，在一个统一的编码器-解码器网络中实现。 该模型通过加入处理信号相关噪声的泊松去噪损失来同时增强照明并抑制噪声。无需事先了解反射率和光照的情况下，网络可以学习有效且一致的分解过程，确保反射率的一致性和光照平滑性而不引起任何形式的颜色失真。实验结果证明了所提出低光照明增强方法的有效性和实用性。我们的方法显著提高了低光环境下的可见度和亮度，同时在环境光照条件下保持图像结构和颜色一致性。
URL
https://arxiv.org/abs/2506.04470
