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https://github.com/dawnlh/awesome-low-light-image-enhancement
This is a resouce list for low light image enhancement
https://github.com/dawnlh/awesome-low-light-image-enhancement
List: awesome-low-light-image-enhancement
computer-vision contrast-enhancement deep-learning histogram-equalization image-enhancement image-enhancment image-processing low-light low-light-image-enhancement retinex
Last synced: 9 days ago
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This is a resouce list for low light image enhancement
- Host: GitHub
- URL: https://github.com/dawnlh/awesome-low-light-image-enhancement
- Owner: dawnlh
- Created: 2020-05-24T03:44:36.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-15T01:48:05.000Z (2 months ago)
- Last Synced: 2024-12-05T15:04:52.559Z (16 days ago)
- Topics: computer-vision, contrast-enhancement, deep-learning, histogram-equalization, image-enhancement, image-enhancment, image-processing, low-light, low-light-image-enhancement, retinex
- Language: MATLAB
- Homepage:
- Size: 17.9 MB
- Stars: 1,501
- Watchers: 35
- Forks: 219
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
- ultimate-awesome - awesome-low-light-image-enhancement - This is a resouce list for low light image enhancement. (Other Lists / Monkey C Lists)
README
# Awesome Low Light Image Enhancement
**This is a resource list for low light image enhancement, including datasets, methods/codes/papers, metrics and so on.**
Looking forward to your sharing! You can come up with your ideas and suggestions in the [issue](https://github.com/dawnlh/low-light-image-enhancement-resources/issues) or directly [pull request](https://github.com/dawnlh/awesome-low-light-image-enhancement/pulls).
## Introduction
Low light imaging and low light image enhancement have wild applications in our daily life and different scientific research fields, like night surveillance, automated driving, fluorescence microscopy, high speed imaging and so on. However, there is still a long way to go in dealing with these tasks, considering the great challenges in low photon counts, low SNR, complicated noise models, etc. Here, we collect a list of resources related to low light image enhancement, including datasets, methods/codes/papers, metrics, and so on. We hope this can help to provide some help to the development of new methods and solutions to the low light tasks.
## Table of Contents
- [Highlights](#highlights)
- [Datasets](#datasets)
- [Review and Benchmark](#review-and-benchmark)
- [Methods](#methods)
* [Learning-based methods](#learning-based-methods)
* [HE-based methods](#he-based-methods)
* [Retinex-based methods](#retinex-based-methods)
* [Other methods](#other-methods)
- [Related Works](#related-works)
- [Metrics](#metrics)
- [Reference](#reference)
## Highlights
:high_brightness: **News!**
- Papers from **ICCV2023** have been updated!
## Datasets
| Dataset | Brief intro | Website |
| :-------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| [SID](https://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Learning_to_See_CVPR_2018_paper.html) | Learning to see in the dark ;
contains 5094 raw shortexposure images, each with a corresponding long-exposure reference image (illuminance level: outdoor scene 0.2 lux - 5 lux; indoor scene: 0.03 lux - 0.3 lux) | [link](https://cchen156.github.io/SID.html) |
| [ExDARK](https://www.sciencedirect.com/science/article/abs/pii/S1077314218304296) | A collection of 7,363 low-light images from very low-light environments to twilight (i.e 10 different conditions) with 12 object classes (similar to PASCAL VOC) annotated on both image class level and local object bounding boxes. | [github](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset) |
| [LOL](https://arxiv.org/abs/1808.04560) | Deep Retinex Decomposition for Low-Light Enhancement | [link](https://daooshee.github.io/BMVC2018website) |
| [SICE](https://ieeexplore.ieee.org/abstract/document/8259342/) | A large-scale multi-exposure image dataset, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images | [github](https://github.com/csjcai/SICE) |
| [MIT-Adobe FiveK](http://people.csail.mit.edu/vladb/photoadjust/db_imageadjust.pdf) | Learning Photographic Global Tonal Adjustment;
a dataset consisting of 5,000 photographs, with both the original RAW images straight from the camera and adjusted versions by 5 trained photographers| [link](https://data.csail.mit.edu/graphics/fivek) |
| [DID](https://openaccess.thecvf.com/content/ICCV2023/papers/Fu_Dancing_in_the_Dark_A_Benchmark_towards_General_Low-light_Video_ICCV_2023_paper.pdf) | A high-quality low-light video dataset with multiple exposures and cameras | [link](https://github.com/ciki000/DID#dancing-in-the-dark-a-benchmark-towards-general-low-light-video-enhancement) |
| DPED | DSLR-quality photos on mobile devices with deep convolutional networks | [link](http://people.ee.ethz.ch/~ihnatova) |
| VIP-LowLight | Eight Natural Images Captured in Very Low-Light Conditions | [link](https://uwaterloo.ca/vision-image-processing-lab/research-demos/vip-lowlight-dataset) |
| ReNOIR | RENOIR - A Dataset for Real Low-Light Image Noise Reduction | [link](http://adrianbarburesearch.blogspot.com/p/renoir-dataset.html) |
| [LLIV-Phone](https://arxiv.org/abs/2104.10729) | The images and videos are taken by various phones' cameras under diverse illumination conditions and scenes | [link](https://drive.google.com/file/d/1QS4FgT5aTQNYy-eHZ_A89rLoZgx_iysR/view?usp=sharing)
| [TM-DIED](https://sites.google.com/site/vonikakis/datasets/tm-died?authuser=0) | 222 JPEG photos constituting some of the most challenging cases for image enhancement and tone-mapping algorithms | [link](https://www.google.com/url?q=https%3A%2F%2Fwww.flickr.com%2Fgp%2F73847677%40N02%2FGRn3G6&sa=D&sntz=1&usg=AOvVaw3mOxOzBNN3OY1jKiRfVN7C) |
| [DRV](https://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Seeing_Motion_in_the_Dark_ICCV_2019_paper.html) | 202 paired raw low-light image Dataset | [link](https://github.com/cchen156/Seeing-Motion-in-the-Dark)
| [LIME](https://ieeexplore.ieee.org/abstract/document/7782813) | A small amount of unpaired images for testing. | [link](https://drive.google.com/file/d/0BwVzAzXoqrSXb3prWUV1YzBjZzg/view) |
| [VV - Phos](https://robotics.pme.duth.gr/research/phos/) | A color image database of 15 scenes captured under different illumination conditions | [link](http://robotics.pme.duth.gr/phos2.html) |
| The 500px Dataset | Exposure: A White-Box Photo Post-Processing Framework | - |
| The Extended Yale Face Database B | The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. | [link](http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/ExtYaleB.html) |
| the nighttime image dataset | A dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms | [link](http://mlg.idm.pku.edu.cn/) |
| VE-LOL | A large-scale low-light image dataset serving both low/high-level vision with diversified scenes and contents as well as complex degradation in real scenarios, called Vision Enhancement in the LOw-Light condition (VE-LOL). | [link](https://flyywh.github.io/IJCV2021LowLight_VELOL/) |
| SDSD | Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment | [github](https://github.com/dvlab-research/SDSD) |
| MID | Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes | [link](https://wenzhengchina.github.io/projects/mid/) |
| DeepHDRVideo | HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset | [link](https://guanyingc.github.io/DeepHDRVideo-Dataset/) |
| LLVIP | LLVIP: A visible-infrared paired dataset for low-light vision | [link](https://bupt-ai-cz.github.io/LLVIP/) |
| RELLISUR | RELLISUR: A Real Low-Light Image Super-Resolution Dataset | [link](https://vap.aau.dk/rellisur/) |
| LSRW | R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network;
3170 paired images using the Nikon camera and 2480 paired images using the Huawei mobile phone. | [github](https://github.com/abcdef2000/R2RNet#dataset) |
| MCR | Mono-colored raw Paired dataset;
a dataset of colored raw and monochrome raw image pairs, captured with the same exposure setting. Each image has a resolution of 1280×1024. Totally 498 different scenes, each scene has 1 corresponding RGB and Monochrome ground truth and 8 different exposure color Raw inputs. | [Google Drive](https://drive.google.com/file/d/1_GWW1P1kjVBMFfN9AuaFq29w-kQ31ncd/view?usp=sharing) [Baidu Netdisk](https://pan.baidu.com/s/1b3cmUenebeDT_8HdLGa9dQ?from=init&pwd=22cv) |
| Raw Image Low-Light Object | - | [link](https://wiki.qut.edu.au/display/cyphy/Datasets) |
| LRAICE | A Learning-to-Rank Approach for Image Color Enhancement | - |
| LOM dataset | A paired low-light & over-exposure & normal-light multi-view dataset (for NeRF under low-light conditions) | [Google Drive](https://drive.google.com/file/d/1orgKEGApjwCm6G8xaupwHKxMbT2s9IAG/view?usp=sharing) [Baidu Netdisk](https://pan.baidu.com/s/1BGfstg2IpN0JZBlVaMG-eQ?pwd=ve1t) |
## Review and Benchmark
| Year | Pub | Paper | Link | Note |
| :--: | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
| 2021 | IJCV | Benchmarking Low-Light Image Enhancement and Beyond | [pdf](https://link.springer.com/article/10.1007%2Fs11263-020-01418-8) | |
| 2021 | IEEE PAMI | Low-Light Image and Video Enhancement Using Deep Learning: A Survey | [pdf](https://doi.org/10.1109/TPAMI.2021.3126387) | |
| 2022 | ArXiv | Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond | [pdf](http://arxiv.org/abs/2212.10772) [code](https://github.com/shenzheng2000/llie_survey) | |
| 2023 | ArXiv | DarkVision: A Benchmark for Low-Light Image/Video Perception | [pdf](https://arxiv.org/abs/2301.06269) | DarkVision |
| 2023 | Signal Process. | A comprehensive experiment-based review of low-light image enhancement methods and benchmarking low-light image quality assessment | [pdf](https://linkinghub.elsevier.com/retrieve/pii/S0165168422003607) | |
## Methods
### Learning-based methods
| Year | Pub | Paper | Link | Note |
| ---- | ----------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------- |
| 2017 | ArXiv | MSR-net:Low-light Image Enhancement Using Deep Convolutional Network | [pdf](https://arxiv.org/pdf/1711.02488v1.pdf) | MSR-net |
| 2017 | ECCV | Deep Burst Denoising | [pdf](http://arxiv.org/abs/1712.05790) | |
| 2017 | VCIP | LLCNN: A convolutional neural network for low-light image enhancement | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305143) [dataset](http://decsai.ugr.es/cvg/dbimagenes/) | LLCNN |
| 2017 | Pattern Recognit. | LLNet: A deep autoencoder approach to natural low-light image enhancement | [pdf](https://doi.org/10.1016/j.patcog.2016.06.008) | LLNet |
| 2017 | ACM Trans. Graph. | Deep bilateral learning for real-time image enhancement | [pdf](https://arxiv.org/abs/1707.02880) [web](https://groups.csail.mit.edu/graphics/hdrnet/) [code](https://github.com/google/hdrnet) | HDRNet |
| 2017 | ICCV | DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks | [pdf](https://arxiv.org/abs/1704.02470) | |
| 2018 | BMVC | Deep Retinex Decomposition for Low-Light Enhancement | [pdf](https://arxiv.org/abs/1808.04560) [web](https://daooshee.github.io/BMVC2018website/) [code](https://github.com/weichen582/RetinexNet) | Retinex-Net |
| 2018 | BMVC | MBLLEN: Low-light Image/Video Enhancement Using CNNs | [pdf](http://bmvc2018.org/contents/papers/0700.pdf) [web](http://phi-ai.org/project/MBLLEN/default.htm) [code](https://github.com/Lvfeifan/MBLLEN) | MBLLEN |
| 2018 | Pattern Recognit. Lett. | LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement | [pdf](https://doi.org/10.1016/j.patrec.2018.01.010) | LightenNet |
| 2018 | CVPR | Learning to See in the Dark | [pdf](https://cchen156.github.io/paper/18CVPR_SID.pdf) [web](https://cchen156.github.io/SID.html) [code](https://github.com/cchen156/Learning-to-See-in-the-Dark.git) [dataset](https://github.com/cchen156/Learning-to-See-in-the-Dark) | |
| 2018 | IEEE TIP | Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images | [pdf](https://doi.org/10.1109/TIP.2018.2794218) [code](https://github.com/csjcai/SICE) | SICE |
| 2018 | ACM TOG | Exposure: A White-Box Photo Post-Processing Framework | [pdf](https://doi.org/10.1145/3181974) [code](https://github.com/yuanming-hu/exposure) | |
| 2018 | FG conference | GLADNet: Low-Light Enhancement Network with Global Awareness | [pdf](https://github.com/daooshee/fgworkshop18Gladnet/blob/master/wwj_fg2018.pdf) [web](https://daooshee.github.io/fgworkshop18Gladnet/) [code](https://github.com/weichen582/GLADNet) [dataset](https://daooshee.github.io/fgworkshop18Gladnet/) | GLADNet |
| 2019 | IEEE TIP | DeepISP: Towards Learning an End-to-End Image Processing Pipeline | [pdf](https://arxiv.org/abs/1801.06724) | DeepISP |
| 2019 | IEEE TIP | Low-Light Image Enhancement via a Deep Hybrid Network | [pdf](https://ieeexplore.ieee.org/document/8692732) | |
| 2019 | IEEE TIP | EnlightenGAN: Deep Light Enhancement without Paired Supervision | [code](https://github.com/TAMU-VITA/EnlightenGAN) [pdf](https://arxiv.org/abs/1906.06972) | EnlightenGAN |
| 2019 | ACM MM | Kindling the Darkness: A Practical Low-light Image Enhancer | [pdf](http://arxiv.org/abs/1905.04161) [code](https://github.com/zhangyhuaee/KinD) [code+](https://github.com/zhangyhuaee/KinD_plus) | KinD |
| 2019 | IEEE Access | A Pipeline Neural Network for Low-Light Image Enhancement | [pdf](https://ieeexplore.ieee.org/document/8607964/) | |
| 2019 | Neurocomputing | Learning Digital Camera Pipeline for Extreme Low-Light Imaging | [pdf](https://arxiv.org/abs/1904.05939) | |
| 2019 | CVPR | Underexposed Photo Enhancement Using Deep Illumination Estimation | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8953588) [code](https://github.com/wangruixing/DeepUPE) | DeepUPE |
| 2019 | ICCV | Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise | [pdf](https://ieeexplore.ieee.org/document/9011000) | |
| 2019 | ICIP | Enhancement of Weakly Illuminated Images by Deep Fusion Networks | [pdf](https://ieeexplore.ieee.org/document/8803041) | |
| 2019 | ICCP | A Bit Too Much? High Speed Imaging from Sparse Photon Counts | [pdf](https://ieeexplore.ieee.org/document/8747325/) | |
| 2019 | ICIP | Llrnet: A Multiscale Subband Learning Approach for Low Light Image Restoration | [pdf](https://ieeexplore.ieee.org/document/8803765) | Llrnet |
| 2019 | ICIP | Low-Lightgan: Low-Light Enhancement Via Advanced Generative Adversarial Network With Task-Driven Training | [pdf](https://ieeexplore.ieee.org/document/8803328) | Low-Lightgan |
| 2019 | ICME | RDGAN: Retinex Decomposition Based Adversarial Learning for Low-Light Enhancement | [code](https://github.com/WangJY06/RDGAN/) [pdf](https://ieeexplore.ieee.org/document/8785047) | RDGAN |
| 2019 | ICMEW | Low-Light Image Enhancement with Attention and Multi-level Feature Fusion | [pdf](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8794872) | |
| 2019 | PRCV | An Effective Network with ConvLSTM for Low-Light Image Enhancement | [pdf](https://doi.org/10.1007/978-3-030-31723-2_19) | |
| 2019 | VISIGRAPP | End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks | [pdf](https://arxiv.org/abs/1904.07483v1) | |
| 2020 | CVPR | Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf) [web](https://li-chongyi.github.io/Proj_Zero-DCE.html) [code](https://github.com/Li-Chongyi/Zero-DCE) | Zero-DCE |
| 2020 | CVPR | Learning to Restore Low-Light Images via Decomposition-and-Enhancement | [pdf](https://ieeexplore.ieee.org/document/9156446) | |
| 2020 | CVPR | From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement | [pdf](http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_From_Fidelity_to_Perceptual_Quality_A_Semi-Supervised_Approach_for_Low-Light_CVPR_2020_paper.pdf) [web](https://github.com/flyywh/CVPR-2020-Semi-Low-Light) [slides](https://github.com/flyywh/CVPR-2020-Semi-Low-Light/blob/master) | DRBN |
| 2020 | CVPR | DeepLPF: Deep Local Parametric Filters for Image Enhancement | [pdf](https://arxiv.org/abs/2003.13985) [code](https://github.com/sjmoran/DeepLPF) | DeepLPF |
| 2020 | IEEE PAMI | Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time | [pdf](https://ieeexplore.ieee.org/document/9206076) [code](https://github.com/HuiZeng/Image-Adaptive-3DLUT) | Image-Adaptive-3DLUT |
| 2020 | IET Image Proc. | Learning an Adaptive Model for Extreme Low-Light Raw Image Processing | [pdf](https://arxiv.org/pdf/2004.10447.pdf) [code](https://github.com/505030475/ExtremeLowLight) | |
| 2020 | ArXiv | Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement | [pdf](http://arxiv.org/abs/2005.07343) [code](https://github.com/MDLW/Low-Light-Image-Enhancement) | |
| 2020 | ArXiv | Self-supervised Image Enhancement Network: Training with Low Light Images Only | [pdf](https://arxiv.org/abs/2002.11300) [code](https://github.com/hitzhangyu/Self-supervised-Image-Enhancement-Network-Training-With-Low-Light-Images-Only) | |
| 2020 | ICPR | Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks | [pdf](http://arxiv.org/abs/2005.02818) | |
| 2021 | IJCV | Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset | [pdf](https://link.springer.com/10.1007/s11263-021-01466-8) [code](https://github.com/yu-li/AGLLNet) | |
| 2021 | CVPR | Retinex-Inspired Unrolling with Cooperative Prior Architecture Search for Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Retinex-Inspired_Unrolling_With_Cooperative_Prior_Architecture_Search_for_Low-Light_Image_CVPR_2021_paper.pdf) [web](http://dutmedia.org/RUAS/) [code](https://github.com/dut-media-lab/RUAS) | RUAS |
| 2021 | CVPR | Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments | [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_Deep_Denoising_of_Flash_and_No-Flash_Pairs_for_Photography_in_CVPR_2021_paper.pdf) [code](https://github.com/likesum/deepFnF) | |
| 2021 | CVPR | Extreme Low-Light Environment-Driven Image Denoising over Permanently Shadowed Lunar Regions with a Physical Noise Model | [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Moseley_Extreme_Low-Light_Environment-Driven_Image_Denoising_Over_Permanently_Shadowed_Lunar_Regions_CVPR_2021_paper.pdf) | HORUS |
| 2021 | CVPR | Learning Temporal Consistency for Low Light Video Enhancement from Single Images | [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.pdf) [code](https://github.com/zkawfanx/StableLLVE) | |
| 2021 | CVPR | Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects | [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Sharma_Nighttime_Visibility_Enhancement_by_Increasing_the_Dynamic_Range_and_Suppression_CVPR_2021_paper.pdf) | |
| 2021 | ICCV | Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Seeing_Dynamic_Scene_in_the_Dark_A_High-Quality_Video_Dataset_ICCV_2021_paper.pdf) [code](https://github.com/dvlab-research/SDSD) | SDSD |
| 2021 | ICCV | HDR Video Reconstruction: A Coarse-to-Fine Network and a Real-World Benchmark Dataset | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_HDR_Video_Reconstruction_A_Coarse-To-Fine_Network_and_a_Real-World_Benchmark_ICCV_2021_paper.pdf) [web](https://guanyingc.github.io/DeepHDRVideo) [code](https://github.com/guanyingc/DeepHDRVideo) | DeepHDRVideo |
| 2021 | ICCV | Matching in the Dark: A Dataset for Matching Image Pairs of Low-Light Scenes | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Matching_in_the_Dark_A_Dataset_for_Matching_Image_Pairs_ICCV_2021_paper.pdf) [web](https://wenzhengchina.github.io/projects/mid/) [code](https://github.com/Wenzhengchina/Matching-in-the-Dark) | MID |
| 2021 | ICCV | Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.pdf) [code](https://github.com/CharlieZCJ/UTVNet/tree/5e76495bf371371a7fc63a521fb6dd9de35ee241) | UTVNet |
| 2021 | ICCVW | LLVIP: A Visible-Infrared Paired Dataset for Low-Light Vision | [pdf](https://openaccess.thecvf.com/content/ICCV2021W/RLQ/papers/Jia_LLVIP_A_Visible-Infrared_Paired_Dataset_for_Low-Light_Vision_ICCVW_2021_paper.pdf) [code](https://github.com/bupt-ai-cz/LLVIP) [web](https://bupt-ai-cz.github.io/LLVIP/) | LLVIP |
| 2021 | JVCIR | R2RNet: Low-Light Image Enhancement via Real-Low to Real-Normal Network | [pdf](http://arxiv.org/abs/2106.14501) [code](https://github.com/abcdef2000/R2RNet) | R2RNet |
| 2022 | CVPR | Toward Fast, Flexible, and Robust Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.html) [code](https://github.com/vis-opt-group/SCI) | SCI |
| 2022 | CVPR | Deep Color Consistent Network for Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Deep_Color_Consistent_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html) | DCC-Net |
| 2022 | CVPR | URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Wu_URetinex-Net_Retinex-Based_Deep_Unfolding_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html) [code](https://github.com/AndersonYong/URetinex-Net) | URetinex-Net |
| 2022 | CVPR | Day-to-Night Image Synthesis for Training Nighttime Neural ISPs | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Punnappurath_Day-to-Night_Image_Synthesis_for_Training_Nighttime_Neural_ISPs_CVPR_2022_paper.html) [code](https://github.com/SamsungLabs/day-to-night) | |
| 2022 | CVPR | SNR-Aware Low-Light Image Enhancement | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SNR-Aware_Low-Light_Image_Enhancement_CVPR_2022_paper.html) [code](https://github.com/dvlab-research/SNR-Aware-Low-Light-Enhance) | |
| 2022 | CVPR | Dancing Under the Stars: Video Denoising in Starlight | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.html) | |
| 2022 | CVPR | Abandoning the Bayer-Filter To See in the Dark | [pdf](https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html) [code](https://github.com/TCL-AILab/Abandon_Bayer-Filter_See_in_the_Dark) | |
| 2022 | ECCV | Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression | [pdf](https://arxiv.org/pdf/2207.10564.pdf) [code](https://github.com/jinyeying/night-enhancement) | |
| 2022 | ECCV | Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction | [pdf](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136790159.pdf) [code](https://github.com/KevinJ-Huang/FECNet) | |
| 2022 | ECCV | LEDNet: Joint Low-Light Enhancement and Deblurring in the Dark | [pdf](https://link.springer.com/chapter/10.1007/978-3-031-20068-7_33) [code](https://github.com/sczhou/LEDNet) | LEDNet |
| 2022 | AAAI | Low-Light Image Enhancement with Normalizing Flow | [pdf](https://arxiv.org/pdf/2109.05923.pdf) [code](https://github.com/wyf0912/LLFlow) [web](https://wyf0912.github.io/LLFlow/) | LLFlow |
| 2022 | AAAI | Semantically contrastive learning for low-light image enhancement | [pdf](https://ojs.aaai.org/index.php/AAAI/article/view/20046) [code](https://github.com/LingLIx/SCL-LLE) [web](https://dongl-group.github.io/project_pages/SCL-LLE.html) | SCL-LLE |
| 2022 | AAAI | DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior | [pdf](https://doi.org/10.1609/aaai.v36i1.19995) | DarkVisionNet |
| 2022 | ACM MM | ChebyLighter: Optimal Curve Estimation for Low-Light Image Enhancement | [pdf](https://dl.acm.org/doi/10.1145/3503161.3548135) [code](https://github.com/eeerpjw/ChebyLighter) | ChebyLighter |
| 2022 | BMCV | You only need 90K parameters to adapt light: a light weight transformer for image enhancement and exposure correction | [pdf](https://bmvc2022.mpi-inf.mpg.de/0238.pdf) [code](https://github.com/cuiziteng/Illumination-Adaptive-Transformer) | IAT |
| 2022 | IJCV | Low-Light Image Enhancement via Breaking down the Darkness | [pdf](http://dx.doi.org/10.1007/s11263-022-01667-9) [code](https://github.com/mingcv/Bread?utm_source=catalyzex.com) | Bread |
| 2022 | Neurocomputing | Low-Light Image Enhancement with Knowledge Distillation | [pdf](https://doi.org/10.1016/j.neucom.2022.10.083) | |
| 2022 | Neurocomputing | LSR: Lightening Super-Resolution Deep Network for Low-Light Image Enhancement | [pdf](https://doi.org/10.1016/j.neucom.2022.07.058) | LSR |
| 2022 | Pattern Recognit. | Brain-like Retinex: A Biologically Plausible Retinex Algorithm for Low Light Image Enhancement | [pdf](http://dx.doi.org/10.1016/j.patcog.2022.109195) | |
| 2022 | Pattern Recognit. | LAE-Net: A Locally-Adaptive Embedding Network for Low-Light Image Enhancement | [pdf](http://dx.doi.org/10.1016/j.patcog.2022.109039) | LAE-Net |
| 2022 | Knowl-Based Syst | LE-GAN: Unsupervised Low-Light Image Enhancement Network Using Attention Module and Identity Invariant Loss | [pdf](https://www.sciencedirect.com/science/article/pii/S0950705121011151) | LE-GAN |
| 2022 | Opt. Lasers Eng. | Infrared and Low-Light Visible Image Fusion Based on Hybrid Multiscale Decomposition and Adaptive Light Adjustment | [pdf](https://doi.org/10.1016/j.optlaseng.2022.107268) | |
| 2022 | Applied Soft Computing | A predictive intelligence approach for low-light enhancement | [pdf](https://linkinghub.elsevier.com/retrieve/pii/S1568494622004173) | |
| 2022 | IEEE TMM | Purifying Low-light Images via Near-Infrared Enlightened Image | [pdf](https://ieeexplore.ieee.org/document/9999306/) | |
| 2022 | IEEE TNNLS | DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations | [pdf](https://ieeexplore.ieee.org/document/9833451/) | |
| 2022 | IEEE TCSVT | EFINet: Restoration for Low-Light Images via Enhancement-Fusion Iterative Network | [pdf](https://ieeexplore.ieee.org/document/9849123/) [code](https://github.com/kyrie111/EFINet) | EFINet |
| 2023 | Information Fusion | A Mutually Boosting Dual Sensor Computational Camera for High Quality Dark Videography | [pdf](https://doi.org/10.1016/j.inffus.2023.01.013) [code](https://github.com/jarrycyx/dual-channel-low-light-video-public) | DCMAN |
| 2023 | Pattern Recognit. | TreEnhance: A tree search method for low-light image enhancement | [pdf](https://www.sciencedirect.com/science/article/pii/S0031320322007282?via%3Dihub) [code](https://github.com/OcraM17/TreEnhance) | TreEnhance |
| 2023 | AAAI | Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method | [pdf](https://arxiv.org/abs/2212.11548) [code](https://github.com/TaoWangzj/LLFormer) [web](https://taowangzj.github.io/projects/LLFormer/) | |
| 2023 | AAAI | Low-Light Video Enhancement with Synthetic Event Guidance | [pdf](https://ojs.aaai.org/index.php/AAAI/article/view/25257) | |
| 2023 | AAAI | Polarization-Aware Low-Light Image Enhancement | [pdf](https://ojs.aaai.org/index.php/AAAI/article/view/25486) [code](https://github.com/fourson/Polarization-Aware-Low-Light-Image-Enhancement) | |
| 2023 | CVPR | DNF: Decouple and feedback network for seeing in the dark | [pdf](https://openaccess.thecvf.com/content/CVPR2023/html/Jin_DNF_Decouple_and_Feedback_Network_for_Seeing_in_the_Dark_CVPR_2023_paper.html) [code](https://github.com/srameo/dnf) | DNF |
| 2023 | CVPR | Learning a simple low-light image enhancer from paired low-light instances | [pdf](http://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.html) [code](https://github.com/zhenqifu/pairlie) | PairLIE |
| 2023 | CVPR | Learning semantic-aware knowledge guidance for low-light image enhancement | [pdf](http://openaccess.thecvf.com/content/CVPR2023/html/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.html) [code](https://github.com/langmanbusi/semantic-aware-low-light-image-enhancement) | SKF |
| 2023 | CVPR | Low-light image enhancement via structure modeling and guidance | [pdf](https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.html) | |
| 2023 | CVPR | Physics-guided ISO-Dependent sensor noise modeling for extreme low-light photography | [pdf](https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Physics-Guided_ISO-Dependent_Sensor_Noise_Modeling_for_Extreme_Low-Light_Photography_CVPR_2023_paper.html) [code](https://github.com/happycaoyue/LLD) | LLD |
| 2023 | CVPR | Visibility constrained wide-band illumination spectrum design for seeing-in-the-dark | [pdf](http://openaccess.thecvf.com/content/CVPR2023/html/Niu_Visibility_Constrained_Wide-Band_Illumination_Spectrum_Design_for_Seeing-in-the-Dark_CVPR_2023_paper.html) [code](https://github.com/myniuuu/vcsd)| VCSD |
| 2023 | IEEE TMM | Glow in the Dark: Low-Light Image Enhancement with External Memory | [pdf](https://ieeexplore.ieee.org/document/10177254/) [code](https://github.com/Lineves7/EMNet) | EMNet |
| 2023 | Mach. Vision Appl. | LDNet: low-light image enhancement with joint lighting and denoising | [pdf](https://link.springer.com/10.1007/s00138-022-01365-z) | LDNet |
| 2023 | IEEE TPAMI | Learning With Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision | [pdf](https://ieeexplore.ieee.org/document/9914672/) [code](https://github.com/vis-opt-group/ruas) | RUAS |
| 2023 | IEEE TIP | TSDN: Two-Stage Raw Denoising in the Dark | [pdf](https://ieeexplore.ieee.org/abstract/document/10168136/) | TSDN |
| 2023 | IEEE TIP | Unsupervised Low-Light Video Enhancement with Spatial-Temporal Co-attention Transformer | [pdf](https://ieeexplore.ieee.org/document/10210621/) | LightenFormer |
| 2023 | IEEE TCYB| Deep Perceptual Image Enhancement Network for Exposure Restoration | [pdf](https://ieeexplore.ieee.org/document/9693338/) | DPIENet |
| 2023 | SIGGRAPH ASIA | Low-light Image Enhancement with Wavelet-based Diffusion Models | [pdf](https://arxiv.org/pdf/2306.00306.pdf) [code](https://github.com/JianghaiSCU/Diffusion-Low-Light) | DiffLL |
| 2023 | ACM MM | CLE Diffusion: Controllable Light Enhancement Diffusion Model | [pdf](https://arxiv.org/abs/2308.06725) [code](https://github.com/YuyangYin/CLEDiffusion) [web](https://yuyangyin.github.io/CLEDiffusion/) | CLE Diffusion |
| 2023 | ACM MM | FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information | [pdf](https://arxiv.org/abs/2308.03033) [code](https://github.com/wangchx67/FourLLIE) | FourLLIE |
| 2023 | Pattern Recognit. | A reflectance re-weighted Retinex model for non-uniform and low-light image enhancement | [pdf](https://linkinghub.elsevier.com/retrieve/pii/S0031320323005216) | |
| 2023 | Pattern Recognit. | SurroundNet: Towards effective low-light image enhancement | [pdf](https://linkinghub.elsevier.com/retrieve/pii/S0031320323003035) [code](https://github.com/ouc-ocean-group/surroundnet)| SurroundNet |
| 2023 | ICCV | Coherent event guided low-light video enhancement | [pdf](https://openaccess.thecvf.com/content/ICCV2023/papers/Liang_Coherent_Event_Guided_Low-Light_Video_Enhancement_ICCV_2023_paper.pdf) [code](https://github.com/sherrycattt/EvLowLight) [web](https://sherrycattt.github.io/EvLowLight/) | EvLowLight |
| 2023 | ICCV | Dancing in the dark: A benchmark towards general low-light video enhancement | [pdf](https://openaccess.thecvf.com/content/ICCV2023/papers/Fu_Dancing_in_the_Dark_A_Benchmark_towards_General_Low-light_Video_ICCV_2023_paper.pdf) [code](https://github.com/ciki000/DID#dancing-in-the-dark-a-benchmark-towards-general-low-light-video-enhancement) | DID |
| 2023 | ICCV | Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model | [pdf](https://arxiv.org/pdf/2308.13164.pdf) | Diff-Retinex |
| 2023 | ICCV | Empowering low-light image enhancer through customized learnable priors | [pdf](http://export.arxiv.org/pdf/2309.01958) [code](https://github.com/zheng980629/CUE) | CUE |
| 2023 | ICCV | ExposureDiffusion: Learning to expose for low-light image enhancement | [pdf](https://arxiv.org/pdf/2307.07710.pdf) [code](https://github.com/wyf0912/ExposureDiffusion) | ExposureDiffusion |
| 2023 | ICCV | Implicit neural representation for cooperative low-light image enhancement | [pdf](https://openaccess.thecvf.com/content/ICCV2023/papers/Yang_Implicit_Neural_Representation_for_Cooperative_Low-light_Image_Enhancement_ICCV_2023_paper.pdf) [code](https://github.com/Ysz2022/NeRCo) | NeRCo |
| 2023 | ICCV | Low-light image enhancement with illumination-aware gamma correction and complete image modelling network | [pdf](https://arxiv.org/pdf/2308.08220.pdf) | COMO-ViT |
| 2023 | ICCV | Low-light image enhancement with multi-stage residue quantization and brightness-aware attention | [pdf](https://openaccess.thecvf.com/content/ICCV2023/papers/Liu_Low-Light_Image_Enhancement_with_Multi-Stage_Residue_Quantization_and_Brightness-Aware_Attention_ICCV_2023_paper.pdf) [code](https://github.com/LiuYunlong99/RQ-LLIE) | RQ-LLIE |
| 2023 | ICCV | Retinexformer: One-stage retinex-based transformer for low-light image enhancement | [pdf](https://arxiv.org/abs/2303.06705) [code](https://github.com/caiyuanhao1998/Retinexformer) | Retinexformer |
| 2023 | ICCV | Lighting up NeRF via unsupervised decomposition and enhancement | [pdf](https://arxiv.org/abs/2307.10664) [code](https://github.com/onpix/LLNeRF) | LLNeRF |
| 2023 | PRICAI | Bootstrap diffusion model curve estimation for high resolution low-light image enhancement | [pdf](https://link.springer.com/chapter/10.1007/978-981-99-7025-4_6) | BDCE |
| 2023 | Appl. Mach. Learn. | LoLi-IEA: low-light image enhancement algorithm | [pdf](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12675/1267512/LoLi-IEA-low-light-image-enhancement-algorithm/10.1117/12.2677422.short#_=_) [code](https://github.com/xingyumex/LoLi-IEA) | LoLi-IEA |
| 2024 | IEEE Sens. Lett. | Integrating Graph Convolution Into a Deep Multilayer Framework for Low-Light Image Enhancement | [pdf](https://ieeexplore.ieee.org/document/10478172/) [code](https://github.com/santoshpanda1995/LightweightGCN-Model) | |
| 2025 | Digital Signal Process. | CDAN: Convolutional dense attention-guided network for low-light image enhancement | [pdf](https://www.sciencedirect.com/science/article/abs/pii/S1051200424004275) [code](https://github.com/SinaRaoufi/CDAN) | CDAN |
### HE-based methods
| Year | Pub | Paper | Link | Note |
| :--: | -------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
| 1990 | IEEE TCE | Contrast limited adaptive histogram equalization: speed and effectiveness | [pdf](https://ieeexplore.ieee.org/document/109340?arnumber=109340) | CLAHE |
| 2007 | IEEE TCE | Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement | [pdf](https://ieeexplore.ieee.org/document/4429280) [code](codes/bpdhe.m) | BPDHE |
| 2007 | IEEE TCE | A Dynamic Histogram Equalization for Image Contrast Enhancement | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4266947) | DHE |
| 2007 | IEEE TCE | Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization | [pdf](https://ieeexplore.ieee.org/document/4266969?arnumber=4266969) | WTHE |
| 2011 | IEEE TIP | Contextual and Variational Contrast Enhancement | [pdf](http://ieeexplore.ieee.org/abstract/document/5773086/) | CVC |
| 2013 | IEEE TIP | Contrast enhancement based on layered difference representation of 2D histograms | [pdf](http://mcl.korea.ac.kr/projects/LDR/2013_tip_cwlee_final_hq.pdf) [web](http://mcl.korea.ac.kr/cwlee_tip2013/) | LDR |
| 2013 | ICASSP | High efficient contrast enhancement using parametric approximation | [pdf](http://150.162.46.34:8080/icassp2013/pdfs/0002444.pdf) | POHE |
- **See also: [link](https://github.com/elliestath/HelpTest/blob/c7e269239a9d67bffc60f44ff1cae70d20770748/docs/Image%20Preprocessing.md)**
### Retinex-based methods
| Year | Pub | Paper | Link | Note |
| ---- | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
| 1997 | IEEE TIP | Properties and performance of a center/surround retinex | [pdf](https://ieeexplore.ieee.org/document/557356) | SSR |
| 1997 | IEEE TIP | A multiscale retinex for bridging the gap between color images and the human observation of scenes | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=597272) [code1](http://www.ipol.im/pub/art/2014/107/) [code2](https://github.com/upcAutoLang/MSRCR-Restoration) | MSRCR |
| 2013 | SITIS | Adaptive Multiscale Retinex for Image Contrast Enhancement | [code](codes/amsr.m) [pdf](https://doi.ieeecomputersociety.org/10.1109/SITIS.2013.19) | AMSR |
| 2013 | IEEE TIP | Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images | [pdf](https://ieeexplore.ieee.org/document/6512558) [web](https://shuhangwang.wordpress.com/2015/12/14/naturalness-preserved-enhancement-algorithm-for-non-uniform-illumination-images/) [code](https://www.dropbox.com/s/096l3uy9vowgs4r/Code.rar) | NPE |
| 2015 | IEEE TIP | A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7229296) [code](codes/PM_SIRE.zip) | SRIE |
| 2016 | CVPR | A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation | [pdf](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Fu_A_Weighted_Variational_CVPR_2016_paper.pdf) [code](codes/WV_SIRE.zip) | SRIE |
| 2016 | Signal Processing | A fusion-based enhancing method for weakly illuminated images | [pdf](https://doi.org/10.1016/j.sigpro.2016.05.031) [code](codes/MF.rar) | MF |
| 2016 | ACM MM | LIME: A Method for Low-light Image Enhancement | [pdf](https://arxiv.org/pdf/1605.05034.pdf ) [web](https://sites.google.com/view/xjguo/lime) [code](https://drive.google.com/file/d/0BwVzAzXoqrSXb3prWUV1YzBjZzg/view) | LIME |
| 2017 | IEEE TIP | LIME: Low-Light Image Enhancement via Illumination Map Estimation | [pdf](http://ieeexplore.ieee.org/document/7782813/) [code1](https://github.com/Sy-Zhang/LIME) [code2](https://github.com/estija/LIME) [code3](https://github.com/pvnieo/Low-light-Image-Enhancement) | LIME |
| 2017 | ICCV | A Joint Intrinsic-Extrinsic Prior Model for Retinex | [pdf](http://caibolun.github.io/papers/JieP.pdf) [web](http://caibolun.github.io/JieP/) [code](https://github.com/caibolun/JieP/) | JieP |
| 2018 | IEEE TIP | Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model | [pdf](https://www.microsoft.com/en-us/research/uploads/prod/2018/04/2018-TIP-Structure-Revealing-Low-Light-Image-Enhancement-Via-Robust-Retinex-Model.pdf) [code1](https://github.com/martinli0822/Low-light-image-enhancement) [code2](codes/robustRetinex.m) | |
| 2018 | Symmetry | A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments | [pdf](https://www.mdpi.com/2073-8994/10/12/718/pdf) | |
| 2019 | Symmetry | Fractional-Order Fusion Model for Low-Light Image Enhancement | [pdf](https://www.mdpi.com/2073-8994/11/4/574/pdf) | |
| 2019 | ICIP | A Hybrid L2 −LP Variational Model For Single Low-Light Image Enhancement With Bright Channel Prior | [pdf](https://ieeexplore.ieee.org/document/8803197) | |
| 2019 | IET Image Proc. | Low light image enhancement based on non-uniform illumination prior model | [pdf](https://ieeexplore.ieee.org/document/8911585) | NIPM |
| 2019 | Comput. Graphics Forum | Dual illumination estimation for robust exposure correction | [pdf](https://arxiv.org/pdf/1910.13688.pdf) [code](https://github.com/pvnieo/Low-light-Image-Enhancement) | |
| 2020 | ic-ETITE | A comparative analysis of illumination estimation based Image Enhancement techniques | [pdf](https://ieeexplore.ieee.org/document/9077919) | |
| 2020 | IEEE TIP | LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9056796) | LR3M |
| 2023 | Vis Comput | Illumination estimation for nature preserving low-light image enhancement | [pdf](https://link.springer.com/article/10.1007/s00371-023-02770-9) | NPLIE |
| 2023 | ICCV | Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model | [pdf](https://arxiv.org/pdf/2308.13164.pdf) | Diff-Retinex |
| 2023 | ICCV | Retinexformer: One-stage retinex-based transformer for low-light image enhancement | [pdf](https://arxiv.org/abs/2303.06705) [code](https://github.com/caiyuanhao1998/Retinexformer) | Retinexformer |
### Other methods
| Year | Pub | Paper | Link | Note |
| :--: | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
| 2008 | IET Image Proc. | Fast centre-surround contrast modification | [pdf](https://ieeexplore.ieee.org/document/4455541) | |
| 2011 | ICME | Fast efficient algorithm for enhancement of low lighting video | [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6012107) [code](codes/XuanDong-Method.m) | |
| 2017 | ICCVW | A New Low-Light Image Enhancement Algorithm Using Camera Response Model | [pdf](http://ieeexplore.ieee.org/document/8265567/) [code](https://github.com/baidut/OpenCE/blob/master/ours/Ying_2017_ICCV.m) | |
| 2017 | ArXiv | A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement | [pdf](http://arxiv.org/abs/1711.00591) [code](https://github.com/baidut/BIMEF) | BIMEF |
| 2017 | ICCAIP | A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework | [pdf](https://link.springer.com/chapter/10.1007%2F978-3-319-64698-5_4) [web](https://baidut.github.io/OpenCE/caip2017.html) [code1](https://github.com/baidut/OpenCE/blob/master/ours/Ying_2017_CAIP.m) [code2](https://github.com/AndyHuang1995/Image-Contrast-Enhancement) | |
| 2019 | IEEE TIP | Low-Light Image Enhancement via the Absorption Light Scattering Model | [pdf](https://doi.org/10.1109/TIP.2019.2922106) | ALSM |
| 2019 | ICIP | Fast Image Enhancement Based on Maximum and Guided Filters | [pdf](https://ieeexplore.ieee.org/document/8803591) | |
## Related Works
| Year | Pub | Paper | Link | Note | Tag |
| :--: | -------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----------- | ---------------- |
| 2012 | IST | Improving the robustness in feature detection by local contrast enhancement | [dataset](https://sites.google.com/site/vonikakis/datasets) | | |
| 2015 | ACM ToG | Automatic Photo Adjustment Using Deep Neural Networks | [web](https://sites.google.com/site/homepagezhichengyan/home/dl_img_adjust) [code](https://github.com/stephenyan1984/dl-image-enhance/wiki) [pdf](https://arxiv.org/abs/1412.7725v2) | | |
| 2018 | CVPR | Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning | [code](https://sites.google.com/view/distort-and-recover/) [pdf](https://doi.org/10.1109/CVPR.2018.00621) | | |
| 2021 | TMM | Recurrent exposure generation for low-light face detection | [pdf](https://arxiv.org/abs/2007.10963) [code](https://github.com/sherrycattt/REGDet) | REGDet | face detection |
| 2021 | CVPR | HLA-Face: Joint High-Low Adaptation for Low Light Face Detection | [web](https://daooshee.github.io/HLA-Face-Website/) [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.pdf) [code](https://github.com/daooshee/HLA-Face-Code) | HLA-Face | face detection |
| 2021 | ICCV | Multitask AET With Orthogonal Tangent Regularity for Dark Object Detection | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Cui_Multitask_AET_With_Orthogonal_Tangent_Regularity_for_Dark_Object_Detection_ICCV_2021_paper.pdf) [code](https://github.com/cuiziteng/ICCV_MAET) | MAET | object detection |
| 2021 | ICCV | Photon-Net: Photon-Starved Scene Inference using Single Photon Cameras | [pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Goyal_Photon-Starved_Scene_Inference_Using_Single_Photon_Cameras_ICCV_2021_paper.pdf) [code](https://github.com/bhavyagoyal/spclowlight/) [video](https://www.youtube.com/watch?v=r1YvHnGbi6k) | Photon-Net | single photon |
| 2021 | ICCVW | Single-Stage Face Detection under Extremely Low-Light Conditions | [pdf](https://openaccess.thecvf.com/content/ICCV2021W/RLQ/papers/Yu_Single-Stage_Face_Detection_Under_Extremely_Low-Light_Conditions_ICCVW_2021_paper.pdf) | | face detection |
| 2021 | ICCVW | DeLiEve-Net: Deblurring Low-Light Images with Light Streaks and Local Events | [pdf](https://www.semanticscholar.org/paper/DeLiEve-Net%3A-Deblurring-Low-light-Images-with-Light-Zhou-Teng/105bf9ccbc749d976ab1f4b455d379f30b1d6508) | DeLiEve-Net | event camera |
| 2022 | ArXiv | An Efficient Low-Light Restoration Transformer for Dark Light Field Images | | LRT | light field |
| 2022 | ICCP| Robust Scene Inference under Noise-Blur Dual Corruptions | [pdf](https://arxiv.org/abs/2207.11643) [code](https://github.com/bhavyagoyal/noiseblurdual) [web](https://wisionlab.com/project/noiseblurdual/) | Noise-Blur Dual | object detection
| 2023 | ICCV | FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision | [pdf](https://openaccess.thecvf.com/content/ICCV2023/papers/Hashmi_FeatEnHancer_Enhancing_Hierarchical_Features_for_Object_Detection_and_Beyond_Under_ICCV_2023_paper.pdf) [code](https://github.com/khurramHashmi/FeatEnHancer) [Web](https://khurramhashmi.github.io/FeatEnHancer/)| FeatEnHancer | object detection and semantic segmentation |
| 2023 | IEEE TIP | INFWIDE: Image and feature space wiener deconvolution network for non-blind image deblurring in low-light conditions | [pdf](https://ieeexplore.ieee.org/document/10047966) [code](https://github.com/zhihongz/INFWIDE) | INFWIDE | deblurring |
| 2024 | AAAI | Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption | [pdf](https://arxiv.org/abs/2312.09093) [code](https://github.com/cuiziteng/Aleth-NeRF) [web](https://cuiziteng.github.io/Aleth_NeRF_web/) | Aleth-NeRF | NeRF |
## Metrics
| Metric | Abbr | Full-/Non-Reference | Link |
| :------------------: | ------ | ------------------------ | ---------------- |
| Peak Signal to Noise Ratio | PSNR | Full-Reference |- |
| Structural Similarity Index Measure | SSIM| Full-Reference | - |
| Learned Perceptual Image Patch Similarity | LPIPS | Full-Reference | [code](https://github.com/richzhang/PerceptualSimilarity) |
| Lightness Order Error | LOE | Non-Reference | [paper](https://ieeexplore.ieee.org/document/6512558) |
| Natural Image Quality Evaluator | NIQE | Non-Reference | [paper](https://ieeexplore.ieee.org/document/6353522)|
| Mean Square Error | MSE | Full-Reference | - |
| Mean Absolute Error | MAE | Full-Reference | - |
| Smartphone Photography Attribute and Quality | SPAQ | Non-Reference | [code](https://github.com/h4nwei/SPAQ) |
| Neural Image Assessment | NIMA | Non-Reference | [pytorch](https://github.com/kentsyx/Neural-IMage-Assessment) [tensorflow](https://github.com/titu1994/neural-image-assessment)|
| Multi-scale Image Quality Transformer | MUSIQ | Non-Reference | [code](https://github.com/google-research/google-research/tree/master/musiq) |
## Reference
- https://github.com/baidut/OpenCE
- https://github.com/tiandaoxiaowu/image-enhancement-about-Retinex
- https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open