https://github.com/tuvovan/nhdrrnet
Keras Implementation of the paper Deep HDR Imaging via A Non-Local Network - TIP 2020
https://github.com/tuvovan/nhdrrnet
keras keras-tensorflow low-light-image-enhancement
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Keras Implementation of the paper Deep HDR Imaging via A Non-Local Network - TIP 2020
- Host: GitHub
- URL: https://github.com/tuvovan/nhdrrnet
- Owner: tuvovan
- License: mit
- Created: 2020-09-11T02:43:06.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-09T09:48:46.000Z (over 4 years ago)
- Last Synced: 2025-04-03T08:05:00.701Z (3 months ago)
- Topics: keras, keras-tensorflow, low-light-image-enhancement
- Language: Python
- Homepage:
- Size: 45.6 MB
- Stars: 52
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep HDR Imaging
The Keras Implementation of the [Deep HDR Imaging via A Non-Local Network](https://ieeexplore.ieee.org/document/8989959) - TIP 2020
## Content
- [Deep-HDR-Imaging](#deep-hdr-imaging)
- [Getting Started](#getting-tarted)
- [Running](#running)
- [References](#references)
- [Citations](#citation)
## Getting Started
- Clone the repository
### Prerequisites
- Tensorflow 2.2.0+
- Tensorflow_addons
- Python 3.6+
- Keras 2.3.0
- PIL
- numpy
```python
pip install -r requirements.txt
```
## Running
### Training
- Preprocess
- Download the [training data](https://cseweb.ucsd.edu/~viscomp/projects/SIG17HDR/PaperData/SIGGRAPH17_HDR_Trainingset.zip) and [testing data](https://cseweb.ucsd.edu/~viscomp/projects/SIG17HDR/PaperData/SIGGRAPH17_HDR_Testset.zip).
- Run this file to generate data. (Please remember to change path first)
```
python src/create_dataset.py
```
- Train NHDRRNet
```
python main.py
```
- Test NHDRRNet
```
python test.py
```
## Usage
### Training
```
usage: main.py [-h] [--images_path IMAGES_PATH] [--test_path TEST_PATH]
[--lr LR] [--gpu GPU] [--num_epochs NUM_EPOCHS]
[--train_batch_size TRAIN_BATCH_SIZE]
[--display_ep DISPLAY_EP] [--checkpoint_ep CHECKPOINT_EP]
[--checkpoints_folder CHECKPOINTS_FOLDER]
[--load_pretrain LOAD_PRETRAIN] [--pretrain_dir PRETRAIN_DIR]
[--filter FILTER] [--kernel KERNEL]
[--encoder_kernel ENCODER_KERNEL]
[--decoder_kernel DECODER_KERNEL]
[--triple_pass_filter TRIPLE_PASS_FILTER]
```
```
optional arguments: -h, --help show this help message and exit
--images_path training path
--lr LR
--gpu GPU
--num_epochs NUM of EPOCHS
--train_batch_size training batch size
--display_ep display result every "x" epoch
--checkpoint_ep save weights every "x" epoch
--checkpoints_folder folder to save weight
--load_pretrain load pretrained model
--pretrain_dir pretrained model folder
--filter default filter
--kernel default kernel
--encoder_kernel encoder filter size
--decoder_kernel decoder filter size
--triple_pass_filter number of filter in triple pass
```
### Testing
The weight file was deprecated. Will be updated soon.
```
usage: test.py [-h] [--test_path TEST_PATH] [--gpu GPU]
[--weight_test_path WEIGHT_TEST_PATH] [--filter FILTER]
[--kernel KERNEL] [--encoder_kernel ENCODER_KERNEL]
[--decoder_kernel DECODER_KERNEL]
[--triple_pass_filter TRIPLE_PASS_FILTER]
```
```
optional arguments: -h, --help show this help message and exit
--test_path test path
--weight_test_path weight test path
--filter default filter
--kernel default kernel
--encoder_kernel encoder filter size
--decoder_kernel decoder filter size
--triple_pass_filter number of filter in triple pass
```
#### Result
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/tuvovan/NHDRRNet/blob/master/LICENSE) file for details
## References
[1] Deep HDR Imaging via A Non-Local Network - TIP 2020 [link](https://ieeexplore.ieee.org/document/8989959)
[3] Training and Testing dataset - [link](https://cseweb.ucsd.edu/~viscomp/projects/SIG17HDR/)
## Citation
```
@ARTICLE{8989959, author={Q. Yan and L. Zhang and Y. Liu and Y. Zhu and J. Sun and Q. Shi and Y. Zhang},
journal={IEEE Transactions on Image Processing},
title={Deep HDR Imaging via A Non-Local Network},
year={2020},
volume={29},
number={},
pages={4308-4322},}
```
## Acknowledgments
- This work based on the paper mentioned above with few modification:
- the fixed size of the adaptive average pooling (16 instead of 32 as assigned in the paper)
- the number of triple pass module is defined as 10 to match the number of 32M as stated in the paper.
- Any ideas on updating or misunderstanding, please send me an email:
- If you find this repo helpful, kindly give me a star.
# Update: I have just released my work on HDR imaging using Attention non-local network. Please check as follow: https://github.com/tuvovan/ANL-HDRI