# R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network

arXiv: https://arxiv.org/abs/2106.14501 · HTML: https://mlsanity.com/paper/2106.14501/

## Key facts

- Cited by 106 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 61 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [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)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [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)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019)
- [MSR-net:Low-light Image Enhancement Using Deep Convolutional Network](https://mlsanity.com/paper/1711.02488/) (2017)
- [A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement](https://mlsanity.com/paper/1711.00591/) (2017)
- [GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling](https://mlsanity.com/paper/1905.11172/) (2019)
- [Joint Enhancement and Denoising Method via Sequential Decomposition](https://mlsanity.com/paper/1804.08468/) (2018)
- [Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement](https://mlsanity.com/paper/1911.11323/) (2019)
- [Pyramid Attention Networks for Image Restoration](https://mlsanity.com/paper/2004.13824/) (2020)
- [Improved Techniques for Learning to Dehaze and Beyond: A Collective Study](https://mlsanity.com/paper/1807.00202/) (2018)

## Built on by (most cited first, 30 of 106)

- [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023)
- [AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion](https://mlsanity.com/paper/2310.10123/) (2023)
- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2302.11831/) (2023)
- [LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models](https://mlsanity.com/paper/2407.08939/) (2024)
- [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023)
- [Zero-Reference Low-Light Enhancement via Physical Quadruple Priors](https://mlsanity.com/paper/2403.12933/) (2024)
- [DarkIR: Robust Low-Light Image Restoration](https://mlsanity.com/paper/2412.13443/) (2024)
- [Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision](https://mlsanity.com/paper/2112.04719/) (2021)
- [Bilevel Fast Scene Adaptation for Low-Light Image Enhancement](https://mlsanity.com/paper/2306.01343/) (2023)
- [Empowering Low-Light Image Enhancer through Customized Learnable Priors](https://mlsanity.com/paper/2309.01958/) (2023)
- [Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations](https://mlsanity.com/paper/2407.12511/) (2024)
- [Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion](https://mlsanity.com/paper/2401.03788/) (2024)
- [Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2212.10772/) (2022)
- [DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.00683/) (2024)
- [Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data](https://mlsanity.com/paper/2211.04700/) (2022)
- [LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond](https://mlsanity.com/paper/2410.09831/) (2024)
- [CWNet: Causal Wavelet Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.10689/) (2025)
- [TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2503.08168/) (2025)
- [RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models](https://mlsanity.com/paper/2603.25502/) (2026)
- [SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency](https://mlsanity.com/paper/2305.08053/) (2023)
- [DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement](https://mlsanity.com/paper/2308.09279/) (2023)
- [Low Light Image Enhancement Challenge at NTIRE 2026](https://mlsanity.com/paper/2604.17669/) (2026)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices](https://mlsanity.com/paper/2507.04277/) (2025)
- [LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting](https://mlsanity.com/paper/2601.15772/) (2026)
- [Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images](https://mlsanity.com/paper/2207.07317/) (2022)
- [Practical exposure correction via compensation](https://mlsanity.com/paper/2212.14245/) (2022)
- [From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement](https://mlsanity.com/paper/2307.16783/) (2023)
- [Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement](https://mlsanity.com/paper/2309.14709/) (2023)

## Abstract (arXiv)

Images captured in weak illumination conditions could seriously degrade the image quality. Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks. In this study, a novel Retinex-based Real-low to Real-normal Network (R2RNet) is proposed for low-light image enhancement, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net. These three subnets are used for decomposing, denoising, contrast enhancement and detail preservation, respectively. Our R2RNet not only uses the spatial information of the image to improve the contrast but also uses the frequency information to preserve the details. Therefore, our model acheived more robust results for all degraded images. Unlike most previous methods that were trained on synthetic images, we collected the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) to satisfy the training requirements and make our model have better generalization performance in real-world scenes. Extensive experiments on publicly available datasets demonstrated that our method outperforms the existing state-of-the-art methods both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions. Our codes and the LSRW dataset are available at: https://github.com/abcdef2000/R2RNet.