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This is a resouce list for low light image enhancement
| Date | Stars |
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| 2026-07-24 | 1840 |
| 2026-07-25 | 1841 |
| 2026-07-28 | 1842 |
| 2026-07-30 | 1842 |
| 2026-08-06 | 1842 |
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# 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 suggestions through [「PR」](https://github.com/zhihongz/awesome-low-light-image-enhancement/pulls). > :bulb: To facilitate reproducibility, papers with open-source code are welcome. ## 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) - [More Reference](#more-reference) ## Highlights :high_brightness: <font color='red'> **News!** </font> ## 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 ; <br /> 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; <br /> 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-
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:59e190030e22dc32, topic:deep-learning
matched fp:59e190030e22dc32, topic:computer-vision