Low-Light Image Enhancement (LLIE) task tends to restore the details and
visual information from corrupted low-light images. Most existing methods learn
the mapping function between low/normal-light images by Deep Neural Networks
(DNNs) on sRGB and HSV color space. Nevertheless, enhancement involves
amplifying image signals, and applying these color spaces to low-light images
with a low signal-to-noise ratio can introduce sensitivity and instability into
the enhancement process. Consequently, this results in the presence of color
artifacts and brightness artifacts in the enhanced images. To alleviate this
problem, we propose a novel trainable color space, named
Horizontal/Vertical-Intensity (HVI). It not only decouples brightness and color
from RGB channels to mitigate the instability during enhancement but also
adapts to low-light images in different illumination ranges due to the
trainable parameters. Further, we design a novel Color and Intensity Decoupling
Network (CIDNet) with two branches dedicated to processing the decoupled image
brightness and color in the HVI space. Within CIDNet, we introduce the
Lightweight Cross-Attention (LCA) module to facilitate interaction between
image structure and content information in both branches, while also
suppressing noise in low-light images. Finally, we conducted 22 quantitative
and qualitative experiments to show that the proposed CIDNet outperforms the
state-of-the-art methods on 11 datasets. The code will be available at
this https URL

提出了一种基于水平 / 垂直强度（HVI）的新型可训练颜色空间，该颜色空间解耦了 RGB 通道的亮度和色彩，有效改善了低光图像增强过程中的颜色和亮度伪影问题。同时，设计了一种名为 CIDNet 的新型颜色和亮度解耦网络，并在该网络中引入了轻量级交叉注意力（LCA）模块，以促进 HVI 空间中图像结构和内容信息之间的交互，同时抑制低光噪声。在 22 个定量和定性实验中，CIDNet 相较于其他先进方法在 11 个数据集上取得更好的效果。