# Low-light Image Enhancement via Breaking Down the Darkness

arXiv: https://arxiv.org/abs/2111.15557 · HTML: https://mlsanity.com/paper/2111.15557/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Replacing Mobile Camera ISP with a Single Deep Learning Model](https://mlsanity.com/paper/2002.05509/) (2020)
- [DeepISP: Towards Learning an End-to-End Image Processing Pipeline](https://mlsanity.com/paper/1801.06724/) (2018)
- [MSR-net:Low-light Image Enhancement Using Deep Convolutional Network](https://mlsanity.com/paper/1711.02488/) (2017)
- [Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement](https://mlsanity.com/paper/1911.11323/) (2019)
- [Deep Learning Markov Random Field for Semantic Segmentation](https://mlsanity.com/paper/1606.07230/) (2016)

## Built on by (most cited first, 30 of 65)

- [OneRestore: A Universal Restoration Framework for Composite Degradation](https://mlsanity.com/paper/2407.04621/) (2024)
- [LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models](https://mlsanity.com/paper/2407.08939/) (2024)
- [Pyramid Diffusion Models For Low-light Image Enhancement](https://mlsanity.com/paper/2305.10028/) (2023)
- [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [ExposureDiffusion: Learning to Expose for Low-light Image Enhancement](https://mlsanity.com/paper/2307.07710/) (2023)
- [You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement](https://mlsanity.com/paper/2402.05809/) (2024)
- [DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.00683/) (2024)
- [LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network](https://mlsanity.com/paper/2406.01028/) (2024)
- [Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance](https://mlsanity.com/paper/2309.08382/) (2023)
- [Learning to See in the Extremely Dark](https://mlsanity.com/paper/2506.21132/) (2025)
- [CWNet: Causal Wavelet Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.10689/) (2025)
- [RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models](https://mlsanity.com/paper/2603.25502/) (2026)
- [From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement](https://mlsanity.com/paper/2307.16783/) (2023)
- [SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device](https://mlsanity.com/paper/2308.08137/) (2023)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement](https://mlsanity.com/paper/2307.09104/) (2023)
- [Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors](https://mlsanity.com/paper/2409.18899/) (2024)
- [Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers](https://mlsanity.com/paper/2410.08688/) (2024)
- [ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer](https://mlsanity.com/paper/2412.02545/) (2024)
- [GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts](https://mlsanity.com/paper/2503.07417/) (2025)
- [Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning](https://mlsanity.com/paper/2312.16246/) (2023)
- [Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks](https://mlsanity.com/paper/2408.08149/) (2024)
- [FLOL: Fast Baselines for Real-World Low-Light Enhancement](https://mlsanity.com/paper/2501.09718/) (2025)
- [SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration](https://mlsanity.com/paper/2508.04041/) (2025)
- [RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement](https://mlsanity.com/paper/2409.04363/) (2024)
- [DarkDiff: Advancing Low-Light Raw Enhancement by Retasking Diffusion Models for Camera ISP](https://mlsanity.com/paper/2505.23743/) (2025)
- [HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.06814/) (2025)
- [Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement](https://mlsanity.com/paper/2511.01510/) (2025)
- [ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement](https://mlsanity.com/paper/2511.13607/) (2025)

## Abstract (arXiv)

Images captured in low-light environment often suffer from complex degradation. Simply adjusting light would inevitably result in burst of hidden noise and color distortion. To seek results with satisfied lighting, cleanliness, and realism from degraded inputs, this paper presents a novel framework inspired by the divide-and-rule principle, greatly alleviating the degradation entanglement. Assuming that an image can be decomposed into texture (with possible noise) and color components, one can specifically execute noise removal and color correction along with light adjustment. Towards this purpose, we propose to convert an image from the RGB space into a luminance-chrominance one. An adjustable noise suppression network is designed to eliminate noise in the brightened luminance, having the illumination map estimated to indicate noise boosting levels. The enhanced luminance further serves as guidance for the chrominance mapper to generate realistic colors. Extensive experiments are conducted to reveal the effectiveness of our design, and demonstrate its superiority over state-of-the-art alternatives both quantitatively and qualitatively on several benchmark datasets. Our code is publicly available at https://github.com/mingcv/Bread.