Summary of You Only Need One Color Space: An Efficient Network For Low-light Image Enhancement, by Qingsen Yan et al.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
by Qingsen Yan, Yixu Feng, Cheng Zhang, Pei Wang, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to Low-Light Image Enhancement (LLIE) using a trainable color space called Horizontal/Vertical-Intensity (HVI). Existing methods learn the mapping function between low and normal-light images using Deep Neural Networks (DNNs) on sRGB and HSV color spaces, but this can introduce sensitivity and instability in the enhancement process. The proposed HVI color space decouples brightness and color from RGB channels, mitigating instability during enhancement, and adapts to different illumination ranges due to trainable parameters. A novel Color and Intensity Decoupling Network (CIDNet) is designed with two branches processing image brightness and color in the HVI space, using a Lightweight Cross-Attention (LCA) module to facilitate interaction between structure and content information while suppressing noise. Experimental results on 11 datasets demonstrate that CIDNet outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make low-light images look better by creating a new way to enhance them. The usual approach uses deep learning models and two types of color spaces, but this can cause problems. The researchers created a new color space called HVI that separates brightness and color from the original image colors. This makes it easier to enhance the image without making it look unnatural or adding noise. They also designed a special network called CIDNet that uses this new color space to enhance images. The results show that their method is better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Cross attention » Deep learning
