Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

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Deep LearningBread
Overview

Low-light Image Enhancement via Breaking Down the Darkness

by Qiming Hu, Xiaojie Guo.

1. Dependencies

  • Python3
  • PyTorch>=1.0
  • OpenCV-Python, TensorboardX
  • NVIDIA GPU+CUDA

2. Network Architecture

figure_arch

3. Data Preparation

3.1. Training dataset

  • 485 low/high-light image pairs from our485 of LOL dataset, each low image of which is augmented by our exposure_augment.py to generate 8 images under different exposures.
  • To train the MECAN (if it is desired), 559 randomly-selected multi-exposure sequences from SICE are adopted.

3.2. Tesing dataset

The images for testing can be downloaded in this link.

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.

5. Quantitative comparison on eval15

table_eval

6. Visual comparison on eval15

figure_eval

7. Visual comparison on DICM

figure_test_dicm

8. Visual comparison on VV and MEF-DS

figure_test_vv_mefds

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Comments
  • How to create data?

    How to create data?

    I have download datasets, but I have no idea about how to creat data. I read the code and found that I need eval/images eval/targets train/images_aug train/targets to train. Could you please tell me how to perpare these for folder? thanks so much!

    opened by Adolfhill 4
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Qiming Hu
Qiming Hu
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