Here are the low-light image enhancement inference code collections.
Thanks to the researchers for contributing to facilitating the low-light image enhancement field. Nevertheless, I find that there are still many inconveniences for followers due to various factors:
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Framework: Some of the models were implemented by Tensorflow, and some were by Pytorch. On the other hand, early works used old-version frameworks, which is not suitable for the recent ones.
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Coding style: Different coders have different coding styles. It occasionally makes the code reading difficult.
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Closed source: Some researchers have not released their codes publicly. For newbies, reproducing codes is hard.
Therefore, I want to create a unified codes repository for low-light image enhancement by the latest PyTorch framework. This repository includes the inference code collections. If I have more time, I will organize training codes later. Welcome to join the project!
install
python 3.6+
pillow
torch
torchvision
or
pip install -r requirements.txt
Download the pre-trained models(OneDrive Password:ymshi) and put "weights" folder into the "dark_infer_collections" folder.
If testing only one model, e.g., ZeroDCE, you can run the code as follows:
python ZeroDCE/infer.py
Then you can see the results in the folder "output".
Pytorch | Tensorflow/Keras | Other framework | Non-public |
---|---|---|---|
✅ DRBN | ⭕️ Retinex-Net | ⭕️ SICE | ⭕️ D&E |
⭕️ DRBN-v2 | ⭕️ GladNet | ⭕️ LightenNet | ⭕️ DLN |
✅ SGM | ⭕️ DeepUPE | ⭕️ LLNet | ⭕️ PRIEN |
✅ EnlightenGAN | ✅ KinD | ⭕️TBEFN | ⭕️ ProRetinex |
✅ ZeroDCE | ✅ KinD++ | ⭕️ExCNet | ⭕️Component-GAN |
✅ ZeroDCE++ | ⭕️ ISSR | ||
✅ DALE | ⭕️ MBLLEN | ||
✅ DSLR | ⭕️ AGLLNet | ||
⭕️ StableLLVE | |||
⭕️ LPNet | |||
✅ ReLLIE | |||
✅ RUAS | |||
⭕️ RRDNet |
The codes are made available for academic research purpose only.