This is the implementation for HDR-GAN: HDR Image Reconstruction From Multi-Exposed LDR Images With Large Motions,
Yuzhen Niu, Jianbin Wu, Wenxi Liu, Wenzhong Guo, Rynson W. H. Lau, in IEEE Transactions on Image Processing, 2021.
In this work, we proposed a novel GAN-based model, HDR-GAN,
which produces high-quality HDR images from multi-exposed LDR images without the need to explicitly align the LDR images.
conda install cudnn=7.6.5=cuda10.0_0
pip install -r requirements.txt
- Download training set of Kalantari's dataset, decompress and put it in
./dataset
folder
dataset/
└── kalantari_dataset
└── train
├── 16-09-28-01
├── 16-09-28-05
├── 16-09-28-06
├── 16-10-10-a-01
├── ....
└── yyy
- Start training
python train.py \
--epoch 256000 \
--train_hw 512 512 \
--batch_size 2 \
--depth 3 \
--unetpps \
--gpu 0 \
--loss_gan \
--gan sphere \
--lr 1e-4
You can change learning rate during training process by creating c{PID}.conf
file with the content as follow:
LR: 1e-5
To monitor training, you can use Tensorboard in .\logs
dir
python test.py \
--unetpps \
--gpu 0 \
--ckpt pretrained/model \
--cus_test_ds dataset/test
The output results are name as xxx_0.hdr
and xxx_1.hdr
, corresponding to the two results of deep HDR supervision in paper, respectively.
You may use Photomatix for tonemapping .hdr
files to obtain better visual effects.
@article{niu2021hdr,
title={HDR-GAN: HDR image reconstruction from multi-exposed ldr images with large motions},
author={Niu, Yuzhen and Wu, Jianbin and Liu, Wenxi and Guo, Wenzhong and Lau, Rynson WH},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={3885--3896},
year={2021},
publisher={IEEE}
}