The Keras Implementation of the Deep HDR Imaging via A Non-Local Network - TIP 2020
- Clone the repository
- Tensorflow 2.2.0+
- Tensorflow_addons
- Python 3.6+
- Keras 2.3.0
- PIL
- numpy
pip install -r requirements.txt-
Preprocess
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Download the training data and testing data.
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Run this file to generate data. (Please remember to change path first)
python src/create_dataset.py -
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Train NHDRRNet
python main.py -
Test NHDRRNet
python test.py
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
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
This project is licensed under the MIT License - see the LICENSE file for details
[1] Deep HDR Imaging via A Non-Local Network - TIP 2020 link
[3] Training and Testing dataset - link
@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},}
- 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: [email protected]
- If you find this repo helpful, kindly give me a star.