| ## [Low-light Image Enhancement via Breaking Down the Darkness](https://arxiv.org/abs/2111.15557) | |
| by Xiaojie Guo, Qiming Hu. | |
| [](https://colab.research.google.com/github/mingcv/Bread/blob/main/bread_demo_uploader.ipynb) (Online Demo) | |
| <!--  --> | |
| ### 1. Dependencies | |
| * Python3 | |
| * PyTorch>=1.0 | |
| * OpenCV-Python, TensorboardX | |
| * NVIDIA GPU+CUDA | |
| ### 2. Network Architecture | |
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| ### 3. Data Preparation | |
| #### 3.1. Training dataset | |
| * 485 low/high-light image pairs from our485 of [LOL dataset](https://daooshee.github.io/BMVC2018website/), each low image of which is augmented by our [exposure_augment.py](https://github.com/mingcv/Bread/blob/main/exposure_augment.py) to generate 8 images under different exposures. ([Download Link for Augmented LOL](https://drive.google.com/file/d/1gyX2kYJWuj3C00eobd49MjRuNbZ29dqN/view?usp=sharing)) | |
| * To train the MECAN (if it is desired), 559 randomly-selected multi-exposure sequences from [SICE](https://github.com/csjcai/SICE) are adopted ([Download Link for a resized version](https://drive.google.com/file/d/1OTNP-QJ3Nade5my04A2iYVTY77IQBEMf/view?usp=sharing)). | |
| #### 3.2. Tesing dataset | |
| The images for testing can be downloaded in [this link](https://github.com/mingcv/Bread/releases/download/checkpoints/data.zip). | |
| <!-- * 15 low/high-light image pairs from eval15 of [LOL dataset](https://daooshee.github.io/BMVC2018website/). | |
| * 44 low-light images from DICM. | |
| * 8 low-light images from NPE. | |
| * 24 low-light images from VV. --> | |
| ### 4. Usage | |
| #### 4.1. Training | |
| * Multi-exposure data synthesis: ```python exposure_augment.py``` | |
| * Train IAN: ```python train_IAN.py -m IAN --comment IAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche``` | |
| * Train ANSN: ```python train_ANSN.py -m1 IAN -m2 ANSN --comment ANSN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche -m1w ./checkpoints/IAN_335.pth``` | |
| * Train CAN: ```python train_CAN.py -m1 IAN -m3 FuseNet --comment CAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche -m1w ./checkpoints/IAN_335.pth``` | |
| * Train MECAN on SICE: ```python train_MECAN.py -m FuseNet --comment MECAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche``` | |
| * Finetune MECAN on SICE and LOL datasets: ```python train_MECAN_finetune.py -m FuseNet --comment MECAN_finetune --batch_size 1 --val_interval 1 --num_epochs 500 --lr 1e-4 --no_sche -mw ./checkpoints/FuseNet_MECAN_for_Finetuning_404.pth``` | |
| #### 4.2. Testing | |
| * *\[Tips\]: Using gamma correction for evaluation with parameter --gc; Show extra intermediate outputs with parameter --save_extra* | |
| * Evaluation: ```python eval_Bread.py -m1 IAN -m2 ANSN -m3 FuseNet -m4 FuseNet --mef --comment Bread+NFM+ME[eval] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth -m4w ./checkpoints/FuseNet_NFM_297.pth``` | |
| * Testing: ```python test_Bread.py -m1 IAN -m2 ANSN -m3 FuseNet -m4 FuseNet --mef --comment Bread+NFM+ME[test] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth -m4w ./checkpoints/FuseNet_NFM_297.pth``` | |
| * Remove NFM: ```python test_Bread_NoNFM.py -m1 IAN -m2 ANSN -m3 FuseNet --mef -a 0.10 --comment Bread+ME[test] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth``` | |
| #### 4.3. Trained weights | |
| Please refer to [our release](https://github.com/mingcv/Bread/releases/tag/checkpoints). | |
| ### 5. Quantitative comparison on eval15 | |
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| ### 6. Visual comparison on eval15 | |
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| ### 7. Visual comparison on DICM | |
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| ### 8. Visual comparison on VV and MEF-DS | |
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