Noise, artifacts, and over-exposure are significant challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a novel Retinex-based method, called ITRE, which suppresses noise and artifacts from the origin of the model, prevents over-exposure throughout the enhancement process. Specifically, we assume that there must exist a pixel which is least disturbed by low light within pixels of same color. First, clustering the pixels on the RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the whole image, which determines that noise is not over-amplified easily. Next, we consider ITR of the image as the initial illumination transmission map to construct a base model for refined transmission map, which prevents artifacts. Additionally, we design an over-exposure module that captures the fundamental characteristics of pixel over-exposure and seamlessly integrate it into the base model. Finally, there is a possibility of weak enhancement when inter-class distance of pixels with same color is too small. To counteract this, we design a Robust-Guard module that safeguards the robustness of the image enhancement process. Extensive experiments demonstrate the effectiveness of our approach in suppressing noise, preventing artifacts, and controlling over-exposure level simultaneously. Our method performs superiority in qualitative and quantitative performance evaluations by comparing with state-of-the-art methods.

翻译：噪声、伪影和过曝光是低光图像增强领域中的重大挑战。现有方法往往难以同时解决这些问题。本文提出一种基于Retinex的新方法，称为ITRE，该方法从模型源头抑制噪声和伪影，并在整个增强过程中防止过曝光。具体而言，我们假设在相同颜色的像素中，必然存在一个受低光影响最小的像素。首先，在RGB颜色空间中对像素进行聚类，以获取整个图像的光照传输比矩阵，该矩阵确保噪声不易被过度放大。接着，我们将图像的光照传输比视为初始光照传输图，构建一个用于优化传输图的基模型，从而防止伪影。此外，我们设计了一个过曝光模块，用于捕捉像素过曝光的基本特征，并将其无缝集成到基模型中。最后，当相同颜色像素的类间距离过小时，可能出现增强效果不足的情况。为解决这一问题，我们设计了一个鲁棒保护模块，以保障图像增强过程的鲁棒性。大量实验表明，我们的方法能同时有效抑制噪声、防止伪影和控制过曝光水平。通过与最先进方法的定性与定量性能评估对比，我们的方法表现出优越性。