# Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks

arXiv: https://arxiv.org/abs/2008.11434 · HTML: https://mlsanity.com/paper/2008.11434/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 18 of 18)

- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition](https://mlsanity.com/paper/2303.04291/) (2023)
- [Learnable Differencing Center for Nighttime Depth Perception](https://mlsanity.com/paper/2306.14538/) (2023)
- [Self-supervised Low Light Image Enhancement and Denoising](https://mlsanity.com/paper/2103.00832/) (2021)
- [Learning to See Low-Light Images via Feature Domain Adaptation](https://mlsanity.com/paper/2312.06723/) (2023)
- [Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement](https://mlsanity.com/paper/2404.17400/) (2024)
- [Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement](https://mlsanity.com/paper/2207.00965/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions](https://mlsanity.com/paper/2304.02978/) (2023)
- [HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.06814/) (2025)
- [Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex](https://mlsanity.com/paper/2604.10359/) (2026)
- [An Efficient Person Clustering Algorithm for Open Checkout-free Groceries](https://mlsanity.com/paper/2208.02973/) (2022)
- [Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition](https://mlsanity.com/paper/2311.02995/) (2023)
- [Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement](https://mlsanity.com/paper/2503.00642/) (2025)
- [Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement](https://mlsanity.com/paper/2504.11896/) (2025)
- [Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective](https://mlsanity.com/paper/2601.17349/) (2026)
- [A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18323/) (2025)
- [Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification](https://mlsanity.com/paper/2608.10512/) (2026)

## Abstract (arXiv)

Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at https://github.com/hitzhangyu/image-enhancement-with-denoise)