Abstract
Images captured under low-light conditions present significant limitations in many applications, as poor lighting can obscure details, reduce contrast, and hide noise. Removing the illumination effects and enhancing the quality of such images is crucial for many tasks, such as image segmentation and object detection. In this paper, we propose a variational method for low-light image enhancement based on the Retinex decomposition into illumination, reflectance, and noise components. A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition. Moreover, our model integrates a novel nonlocal gradient-type fidelity term designed to preserve structural details. Additionally, we propose an automatic gamma correction module. Building on the proposed variational approach, we extend the model by introducing its deep unfolding counterpart, in which the proximal operators are replaced with learnable networks. We propose cross-attention mechanisms to capture long-range dependencies in both the nonlocal prior of the reflectance and the nonlocal gradient-based constraint. Experimental results demonstrate that both methods compare favorably with several recent and state-of-the-art techniques across different datasets. In particular, despite not relying on learning strategies, the variational model outperforms most deep learning approaches both visually and in terms of quality metrics.
Abstract (translated)
在低光条件下拍摄的图像在许多应用中存在重大限制，因为不良照明会遮盖细节、降低对比度并隐藏噪声。去除光照效果并提升这类图像的质量对于诸如图像分割和目标检测等任务至关重要。本文提出了一种基于Retinex分解（将图像分为光照、反射率和噪声组件）的低光图像增强变分方法。在处理过程中，首先对低光图像进行颜色校正预处理，然后将其用作分解中的观察输入。此外，我们的模型整合了一个新颖的非局部梯度型保真项，旨在保留结构细节。我们还提出了一种自动gamma校正模块。基于所提出的变分方法，我们将该模型扩展为深度解包版本，在此版本中，近似算子被可学习网络取代。我们在反射率的非局部先验和基于非局部梯度约束中引入了跨注意力机制来捕捉长距离依赖关系。实验结果表明，无论是在多个数据集上与多种近期及最先进的技术相比，这两种方法都表现出色。特别地，尽管不依赖于学习策略，变分模型在视觉质量和质量指标方面均优于大多数深度学习方法。
URL
https://arxiv.org/abs/2504.07810
