Abstract
Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images. However, existing standard RGB (sRGB) color space-based LLIE methods often produce color bias and brightness artifacts due to the inherent high color sensitivity. While Hue, Saturation, and Value (HSV) color space can decouple brightness and color, it introduces significant red and black noise artifacts. To address this problem, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by the HV color map and learnable intensity. The HV color map enforces small distances for the red coordinates to remove red noise artifacts, while the learnable intensity compresses the low-light regions to remove black noise artifacts. Additionally, we introduce the Color and Intensity Decoupling Network+ (HVI-CIDNet+), built upon the HVI color space, to restore damaged content and mitigate color distortion in extremely dark regions. Specifically, HVI-CIDNet+ leverages abundant contextual and degraded knowledge extracted from low-light images using pre-trained vision-language models, integrated via a novel Prior-guided Attention Block (PAB). Within the PAB, latent semantic priors can promote content restoration, while degraded representations guide precise color correction, both particularly in extremely dark regions through the meticulously designed cross-attention fusion mechanism. Furthermore, we construct a Region Refinement Block that employs convolution for information-rich regions and self-attention for information-scarce regions, ensuring accurate brightness adjustments. Comprehensive results from benchmark experiments demonstrate that the proposed HVI-CIDNet+ outperforms the state-of-the-art methods on 10 datasets.
Abstract (translated)
低光图像增强（LLIE）的目标是从受损的低光图像中恢复生动的内容和细节。然而，现有的基于标准RGB（sRGB）色彩空间的LLIE方法常常会产生色偏和亮度伪影，因为这种颜色空间对颜色变化非常敏感。尽管HSV色彩空间可以分离亮度和颜色，但它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用于LLIE的颜色空间，即水平/垂直强度（HVI），由HV色图和可学习的强度定义。HV色图强制执行红色坐标的短距离以移除红色噪点伪影，而可学习的强度则压缩低光区域以去除黑色噪点伪影。 此外，我们引入了一种基于HVI颜色空间构建的颜色和强度解耦网络+（HVI-CIDNet+），用于恢复受损内容并减轻极暗区域中的色彩失真。具体来说，HVI-CIDNet+利用预训练的视觉-语言模型从低光图像中提取丰富的上下文和降级知识，并通过一种新颖的先验引导注意模块（PAB）集成这些知识。在PAB内部，潜在语义先验可以促进内容恢复，而退化的表示则指导精确的颜色校正，在极暗区域尤其如此，这是通过精心设计的交叉注意力融合机制实现的。 此外，我们构建了一个区域精炼块，该块采用卷积处理信息丰富的区域，并使用自注意处理信息匮乏的区域，确保准确的亮度调整。基准实验的全面结果表明，所提出的HVI-CIDNet+在10个数据集上优于当前最先进的方法。
URL
https://arxiv.org/abs/2507.06814
