pengjingchao / gsanet

Code used in this work will be publicly available upon publication.

Gamma-enhanced with spatial attention Network for Efficient High Dynamic Range Imaging

By Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chengming Liu and Yizhen Cao

This project is supported by UHD TV Research and Application Laboratory

Now you can read the paper in there. The code framework is inspired by HDRUNet.

Overview

Overview of the network:

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Getting Started

1. Dataset
2. Configuration
3. How to test
4. How to train
5. Visualization

Dataset

Register a codalab account and log in, then find the download link on this page:

https://codalab.lisn.upsaclay.fr/competitions/1514#participate-get-data

It is strongly recommended to use the data provided by the competition organizer for training and testing, or you need at least a basic understanding of the competition data. Otherwise, you may not get the desired result.

Configuration

pip install -r requirements.txt

How to test

• Modify dataroot_LDRs and pretrain_model_G (you can also use the pretrained model which is provided in the ./pretrained_model) in ./codes/options/test/test_HDR.yml, prepare 'results' folder, then run

cd codes
python test.py -opt options/test/test_HDR.yml

The test results will be saved to ./results/testset_name.

How to train

• Prepare the data. Modify input_folder and save_folder in ./scripts/extract_subimgs_single.py and prepare 'experiments' folder, then run

cd scripts
python extract_subimgs_single.py

• Modify dataroot_short 、dataroot_medium 、dataroot_long、dataroot_exp and dataroot_ratio 、dataroot_GT of train and val in ./codes/options/train/train_HDR.yml, then run

cd codes
python train.py -opt options/train/train_HDR.yml

The models and training states will be saved to ./experiments/name.

Measure operations and runtime

In ./scripts, several scripts are available. calculate_ops_example.py are provided by the competition organizer for measuring operations and runtime.

Visualization

In ./scripts, several scripts are available. data_io.py and metrics.py are provided by the competition organizer for reading/writing data and evaluation. Based on these codes, I provide a script for visualization by using the tone-mapping provided in metrics.py. Modify paths of the data in ./scripts/tonemapped_visualization.py and run

cd scripts
python tonemapped_visualization.py

to visualize the images.

Our model

Now our model is not public in there for now. If you want to get it, please contact [email protected] for help. There is just for the framework record.

Acknowledgment

The code is inspired by HDRUNet.