Abstract
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity. The former enforces small distances for red coordinates to remove the red artifacts, while the latter compresses the low-light regions to remove the black artifacts. To fully leverage the chromatic and intensity information, a novel Color and Intensity Decoupling Network (CIDNet) is further introduced to learn accurate photometric mapping function under different lighting conditions in the HVI space. Comprehensive results from benchmark and ablation experiments show that the proposed HVI color space with CIDNet outperforms the state-of-the-art methods on 10 datasets. The code is available at this https URL.
Abstract (translated)
低光图像增强（LLIE）是一项重要的计算机视觉任务，旨在从受污染的低光照图像中恢复详细的视觉信息。许多现有的LLIE方法基于标准RGB（sRGB）空间，由于sRGB固有的高色彩敏感性，在此空间内生成的颜色偏差和亮度伪影较为常见。尽管使用色调、饱和度和值（HSV）颜色空间转换图像有助于解决亮度问题，但会引入显著的红色和黑色噪点伪影。为了解决这个问题，我们提出了一种新的LLIE专用颜色空间——水平/垂直强度（HVI），它由极化HS图和可学习强度定义。前者通过确保红色坐标的短距离来消除红色伪影，而后者则通过压缩低光照区域来移除黑色噪点。 为了充分利用色度和强度信息，我们进一步引入了一种新的网络——颜色与强度解耦网络（CIDNet），用于在HVI空间下学习不同照明条件下的准确光度映射函数。基准测试和消融实验的全面结果显示，在10个数据集上，所提出的带有CIDNet的HVI颜色空间优于当前最先进的方法。代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2502.20272
