  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 is available at
https://github.com/Fediory/HVI-CIDNet.
