MIRNet
Learning Enriched Features for Real Image Restoration and Enhancement
Content
Getting Started
- Clone the repository
Prerequisites
- Tensorflow 2.2.0+
- Python 3.6+
- Keras 2.3.0
- PIL
- numpy
pip install -r requirements.txtRunning
Image Denoising
Training
- Get the dataset
wget https://competitions.codalab.org/my/datasets/download/a26784fe-cf33-48c2-b61f-94b299dbc0f2 - Training
python train_denoise.py
Testing
- Test
python test_denoise.py
Usage
usage: train_denoise.py [-h] [--lr LR] [--gpu GPU]
[--grad_clip_norm GRAD_CLIP_NORM]
[--num_epochs NUM_EPOCHS]
[--train_batch_size TRAIN_BATCH_SIZE]
[--checkpoint_ep CHECKPOINT_EP]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB]
[--num_channels NUM_CHANNELS]
optional arguments:
-h, --help show this help message and exit
--lr LR
--gpu GPU
--grad_clip_norm GRAD_CLIP_NORM
--num_epochs NUM_EPOCHS
--train_batch_size TRAIN_BATCH_SIZE
--checkpoint_ep CHECKPOINT_EP
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
--num_channels NUM_CHANNELS
Download the weight here and put it to the weights/denoise folder.
usage: test_denoise.py [-h] [--test_path TEST_PATH] [--gpu GPU]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB]
[--num_channels NUM_CHANNELS]
optional arguments:
-h, --help show this help message and exit
--test_path TEST_PATH
--gpu GPU
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
--num_channels NUM_CHANNELS
Image Super Resolution
Training
- Get the dataset here
- Training
python train_super.py
Testing
- Test
python test_super.py
Usage
usage: train_super.py [-h] [--lr LR] [--gpu GPU]
[--grad_clip_norm GRAD_CLIP_NORM]
[--num_epochs NUM_EPOCHS]
[--train_batch_size TRAIN_BATCH_SIZE]
[--checkpoint_ep CHECKPOINT_EP]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB] [--mode MODE]
[--scale_factor SCALE_FACTOR]
optional arguments:
-h, --help show this help message and exit
--lr LR
--gpu GPU
--grad_clip_norm GRAD_CLIP_NORM
--num_epochs NUM_EPOCHS
--train_batch_size TRAIN_BATCH_SIZE
--checkpoint_ep CHECKPOINT_EP
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
--mode MODE
--scale_factor SCALE_FACTOR
Download the weight here and put it to the weights/super folder.
usage: test_super.py [-h] [--test_path TEST_PATH] [--gpu GPU]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB]
[--num_channels NUM_CHANNELS]
[--scale_factor SCALE_FACTOR]
optional arguments:
-h, --help show this help message and exit
--test_path TEST_PATH
--gpu GPU
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
--num_channels NUM_CHANNELS
--scale_factor SCALE_FACTOR
Image Enhancement
Training
- Get the dataset here
- Training
python train_delight.py
Testing
- Test
python test_delight.py
Usage
usage: train_delight.py [-h] [--lr LR] [--gpu GPU]
[--grad_clip_norm GRAD_CLIP_NORM]
[--num_epochs NUM_EPOCHS]
[--train_batch_size TRAIN_BATCH_SIZE]
[--checkpoint_ep CHECKPOINT_EP]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB] [--mode MODE]
optional arguments:
-h, --help show this help message and exit
--lr LR
--gpu GPU
--grad_clip_norm GRAD_CLIP_NORM
--num_epochs NUM_EPOCHS
--train_batch_size TRAIN_BATCH_SIZE
--checkpoint_ep CHECKPOINT_EP
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
--mode MODE
Download the weight here and put it to the weights/delight folder.
usage: test_delight.py [-h] [--test_path TEST_PATH] [--gpu GPU]
[--checkpoint_filepath CHECKPOINT_FILEPATH]
[--num_rrg NUM_RRG] [--num_mrb NUM_MRB]
[--num_channels NUM_CHANNELS]
optional arguments:
-h, --help show this help message and exit
--test_path TEST_PATH
--gpu GPU
--checkpoint_filepath CHECKPOINT_FILEPATH
--num_rrg NUM_RRG
--num_mrb NUM_MRB
Result
Image Denoising
| Input - Noisy | Output Denoised |
|---|---|
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Image Enhancement
| Input - Lowlight | Output Delighted |
|---|---|
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Image Super Resolution
| Input - LowRes | Output Bicubic | Output HighRes |
|---|---|---|
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License
This project is licensed under the MIT License - see the LICENSE file for details
References
- Training dataset
Citation
@misc{zamir2020learning,
title={Learning Enriched Features for Real Image Restoration and Enhancement},
author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang and Ling Shao},
year={2020},
eprint={2003.06792},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgments
- Any ideas on updating or misunderstanding, please send me an email: [email protected]
- If you find this repo helpful, kindly give me a star.