mingcv/Bread - Githubissues

Low-light Image Enhancement via Breaking Down the Darkness

by Xiaojie Guo, Qiming Hu.

:boom: Update Online Replicate Demo:

▶️ Online Colab Demo:

📖 Papers: [[arxiv](https://arxiv.org/abs/2111.15557)] [[IJCV](https://link.springer.com/article/10.1007/s11263-022-01667-9)] ### 1. Dependencies * Python3 * PyTorch>=1.0 * OpenCV-Python, TensorboardX * NVIDIA GPU+CUDA ### 2. Network Architecture ![figure_arch](https://github.com/mingcv/Bread/blob/main/figures/Bread_architecture_full.png) ### 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).

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