# You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement

arXiv: https://arxiv.org/abs/2402.05809 · HTML: https://mlsanity.com/paper/2402.05809/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [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)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model](https://mlsanity.com/paper/2308.13164/) (2023)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [Global Structure-Aware Diffusion Process for Low-Light Image Enhancement](https://mlsanity.com/paper/2310.17577/) (2023)
- [U-Net Transformer: Self and Cross Attention for Medical Image Segmentation](https://mlsanity.com/paper/2103.06104/) (2021)
- [Pyramid Diffusion Models For Low-light Image Enhancement](https://mlsanity.com/paper/2305.10028/) (2023)
- [Low-light Image Enhancement via Breaking Down the Darkness](https://mlsanity.com/paper/2111.15557/) (2021)
- [LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement](https://mlsanity.com/paper/2401.15204/) (2024)
- [Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2212.10772/) (2022)
- [JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement](https://mlsanity.com/paper/2312.12826/) (2023)

## Built on by (most cited first, 24 of 24)

- [DarkIR: Robust Low-Light Image Restoration](https://mlsanity.com/paper/2412.13443/) (2024)
- [JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration](https://mlsanity.com/paper/2504.04158/) (2025)
- [Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration](https://mlsanity.com/paper/2501.16583/) (2025)
- [UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration](https://mlsanity.com/paper/2505.06683/) (2025)
- [UniCoRN: Latent Diffusion-based Unified Controllable Image Restoration Network across Multiple Degradations](https://mlsanity.com/paper/2503.15868/) (2025)
- [Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration](https://mlsanity.com/paper/2410.04811/) (2024)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results](https://mlsanity.com/paper/2501.10343/) (2025)
- [FLOL: Fast Baselines for Real-World Low-Light Enhancement](https://mlsanity.com/paper/2501.09718/) (2025)
- [Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration](https://mlsanity.com/paper/2603.29773/) (2026)
- [From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision](https://mlsanity.com/paper/2408.09650/) (2024)
- [OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition](https://mlsanity.com/paper/2503.23266/) (2025)
- [DARK: Denoising, Amplification, Restoration Kit](https://mlsanity.com/paper/2405.12891/) (2024)
- [LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset](https://mlsanity.com/paper/2503.19804/) (2025)
- [Technique Report of CVPR 2024 PBDL Challenges](https://mlsanity.com/paper/2406.10744/) (2024)
- [LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration](https://mlsanity.com/paper/2411.15740/) (2024)
- [Rethinking Model Redundancy for Low-light Image Enhancement](https://mlsanity.com/paper/2412.16459/) (2024)
- [PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement](https://mlsanity.com/paper/2505.09196/) (2025)
- [Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image Enhancement](https://mlsanity.com/paper/2505.11246/) (2025)
- [RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation](https://mlsanity.com/paper/2509.23480/) (2025)
- [UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration](https://mlsanity.com/paper/2511.18152/) (2025)
- [UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration](https://mlsanity.com/paper/2603.07406/) (2026)
- [NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2605.02212/) (2026)
- [Enlight: Fast Low-Light Image Enhancement via Multi-Objective Optimization and Shadow-Aware Refinement](https://mlsanity.com/paper/2606.21674/) (2026)

## Abstract (arXiv)

Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. Most existing methods learn the mapping function between low/normal-light images by Deep Neural Networks (DNNs) on sRGB and HSV color space. Nevertheless, enhancement involves amplifying image signals, and applying these color spaces to low-light images with a low signal-to-noise ratio can introduce sensitivity and instability into the enhancement process. Consequently, this results in the presence of color artifacts and brightness artifacts in the enhanced images. To alleviate this problem, we propose a novel trainable color space, named Horizontal/Vertical-Intensity (HVI). It not only decouples brightness and color from RGB channels to mitigate the instability during enhancement but also adapts to low-light images in different illumination ranges due to the trainable parameters. Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space. Within CIDNet, we introduce the Lightweight Cross-Attention (LCA) module to facilitate interaction between image structure and content information in both branches, while also suppressing noise in low-light images. Finally, we conducted 22 quantitative and qualitative experiments to show that the proposed CIDNet outperforms the state-of-the-art methods on 11 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.