F-Conv_MindSpore/SRExp

• Usage of The Codes

Usage of The Codes

Folder structure:

Data\    : data folder, the Div2k data should be download here 
src\     : Main codes
Demo.txt : Comands for calling all the experiments

Usage:

The code is built on EDSR (PyTorch) and has been tested on both Ubuntu and Windows.
please refer EDSR (PyTorch) for more usage details.

We used DIV2K dataset to train our model. Please download it from here (7.1GB). Unpack the tar file to Data folder before using the codes, or Unpack the tar file to any place you want. Then, change the dir_data argument in src/option.py to the place where DIV2K images are located.

We used Urban100, B100, Set14 and Set5 dataset for testing, which can be download from benchmark datasets (250MB), and Unpack the tar file to the Data folder.

To train and test the proposed method, Cd to 'src', and following scripts are example for training and testing, respectively:

python main.py --model RDN_fcnn --scale 2 --save RDN_fcnn_x2  --res_scale 0.1 --ini_scale  0.1 --batch_size 16  --patch_size 64 --G0 8 --kernel_size 5 --epochs 150 --decay 3-100-130 --lr 4e-4
python main.py --text_only --model RDN_fcnn --scale 2 --save RDN_fcnn_x2  --res_scale 0.1 --ini_scale  0.1 --batch_size 16  --patch_size 64 --G0 8 --kernel_size 5 --epochs 150 --decay 3-100-130 --lr 4e-4

More examples can be found in Demo.txt.

Citation:

Qi Xie, Qian Zhao, Zongben Xu and Deyu Meng*. 
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions[J]. 
IEEE transactions on pattern analysis and machine intelligence, 2022.

BibTeX:

@article{xie2020MHFnet,
title={Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions},
author={Xie, Qi and Zhao, Qian and Xu, Zongben and Meng, Deyu},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}