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awesome-low-light-image-enhancement
This is a resouce list for low light image enhancement
https://github.com/dawnlh/awesome-low-light-image-enhancement
Last synced: about 18 hours ago
JSON representation
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Datasets
- DID - 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) |
- link
- github
- link
- link
- github
- SICE - 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
- link
- link
- link
- LLIV-Phone - eHZ_A89rLoZgx_iysR/view?usp=sharing)
- TM-DIED - 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 - light image Dataset | [link](https://github.com/cchen156/Seeing-Motion-in-the-Dark)
- LIME
- VV - Phos
- link
- link
- link
- link
- link
- Google Drive
- LOL - Light Enhancement | [link](https://daooshee.github.io/BMVC2018website) |
- SICE - 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) |
- Google Drive - eQ?pwd=ve1t) |
- SID - 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 - 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) |
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Methods
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Learning-based methods
- code
- code
- pdf - net |
- pdf - ai.org/project/MBLLEN/default.htm) [code](https://github.com/Lvfeifan/MBLLEN) | MBLLEN |
- pdf - to-See-in-the-Dark.git) [dataset](https://github.com/cchen156/Learning-to-See-in-the-Dark) | |
- pdf - Lightgan |
- pdf - chongyi.github.io/Proj_Zero-DCE.html) [code](https://github.com/Li-Chongyi/Zero-DCE) | Zero-DCE |
- pdf - Adaptive-3DLUT) | Image-Adaptive-3DLUT |
- pdf - Light-Image-Enhancement) | |
- pdf - supervised-Image-Enhancement-Network-Training-With-Low-Light-Images-Only) | |
- pdf - li/AGLLNet) | |
- pdf - media-lab/RUAS) | RUAS |
- pdf - research/SDSD) | SDSD |
- pdf - in-the-Dark) | MID |
- pdf - ai-cz/LLVIP) [web](https://bupt-ai-cz.github.io/LLVIP/) | LLVIP |
- pdf - opt-group/SCI) | SCI |
- pdf - Net |
- pdf - Net) | URetinex-Net |
- pdf - to-night) | |
- pdf - research/SNR-Aware-Low-Light-Enhance) | |
- pdf - AILab/Abandon_Bayer-Filter_See_in_the_Dark) | |
- pdf - enhancement) | |
- pdf - Huang/FECNet) | |
- pdf - LLE) [web](https://dongl-group.github.io/project_pages/SCL-LLE.html) | SCL-LLE |
- pdf - Adaptive-Transformer) | IAT |
- pdf - Net |
- pdf - GAN |
- pdf - channel-low-light-video-public) | DCMAN |
- pdf - Aware-Low-Light-Image-Enhancement) | |
- pdf - aware-low-light-image-enhancement) | SKF |
- pdf - Low-Light) | DiffLL |
- pdf - ocean-group/surroundnet)| SurroundNet |
- pdf - ViT |
- pdf - LLIE) | RQ-LLIE |
- pdf - hu/exposure) | |
- pdf - Model) | |
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Retinex-based methods
- pdf - Retinex |
- pdf - Restoration) | MSRCR |
- pdf - Zhang/LIME) [code2](https://github.com/estija/LIME) [code3](https://github.com/pvnieo/Low-light-Image-Enhancement) | LIME |
- pdf - light-image-enhancement) [code2](codes/robustRetinex.m) | |
- pdf - light-Image-Enhancement) | |
- pdf - Restoration) | MSRCR |
- pdf - Restoration) | MSRCR |
- pdf - Restoration) | MSRCR |
- pdf - Restoration) | MSRCR |
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HE-based methods
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Other methods
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Review and Benchmark
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Related Works
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Metrics
Programming Languages
Sub Categories
Keywords
gan
2
dynamic-video-dataset
1
low-light-video-enhancement
1
cnn
1
computer-vision
1
deep-learning
1
iccv2021
1
image-fusion
1
image-to-image-translation
1
low-light-image
1
low-light-vision
1
object-detection
1
visible-infrared
1
generative-adversarial-networks
1
low-light
1
low-light-enhance
1
pytorch
1
unsupervised-learning
1