# Color-wise Attention Network for Low-light Image Enhancement

arXiv: https://arxiv.org/abs/1911.08681 · HTML: https://mlsanity.com/paper/1911.08681/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [Face Relighting with Geometrically Consistent Shadows](https://mlsanity.com/paper/2203.16681/) (2022)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [Frequency Domain Loss Function for Deep Exposure Correction of Dark Images](https://mlsanity.com/paper/2104.10856/) (2021)
- [Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement](https://mlsanity.com/paper/2307.09104/) (2023)
- [LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset](https://mlsanity.com/paper/2503.19804/) (2025)
- [Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2308.02776/) (2023)
- [Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions](https://mlsanity.com/paper/2410.01225/) (2024)
- [From Fog to Failure: The Unintended Consequences of Dehazing on Object Detection in Clear Images](https://mlsanity.com/paper/2502.02027/) (2025)
- [Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification](https://mlsanity.com/paper/2608.10512/) (2026)

## Abstract (arXiv)

Absence of nearby light sources while capturing an image will degrade the visibility and quality of the captured image, making computer vision tasks difficult. In this paper, a color-wise attention network (CWAN) is proposed for low-light image enhancement based on convolutional neural networks. Motivated by the human visual system when looking at dark images, CWAN learns an end-to-end mapping between low-light and enhanced images while searching for any useful color cues in the low-light image to aid in the color enhancement process. Once these regions are identified, CWAN attention will be mainly focused to synthesize these local regions, as well as the global image. Both quantitative and qualitative experiments on challenging datasets demonstrate the advantages of our method in comparison with state-of-the-art methods.