- Python 3.6
- PyTorch 1.3
- Clone this repo.
git clone https://github.com/yhjo09/AdaTarget
cd AdaTarget
- Download pretrained models and extract it.
unzip models.zip
- Download a zip file containing the images of the Gaussian8 test set, and extract it.
unzip Gaussian8.zip
- The images were generated by following the instructions in the IKC repository.
- Run.
python test_Iso.py
- Output images will be in
./output/Iso.
unzip DIV2KRK.zip
- Run.
python test_RK.py
- Output images will be in
./output/RK.
- Download a zip file contains the images of the Set5, Set14, BSDS100, Urban100, and Manga109 test sets, and extract it.
unzip Bicubic.zip
- Run.
python test_Bic.py
- Output images will be in
./output/Bic.
Soon
- Prepare training images.
- Download DIV2K HR images (from https://data.vision.ee.ethz.ch/cvl/DIV2K) and unzip it to
./DIV2K/DIV2K_train_HR/*.png. - Download DIV2K LR images and unzip it to
./DIV2K/DIV2K_train_LR_RK/*.png.- The LR images were generated by using https://github.com/assafshocher/BlindSR_dataset_generator with random kernels.
- Prepare validation images.
- Download DIV2K HR images and unzip it to
./DIV2K/DIV2K_valid_HR/*.png. - Download DIV2K LR images and unzip it to
./DIV2K/DIV2K_valid_LR_RK/*.png.
-
Specify the pretrained parameters of the localization network to variable
PRETRAINED_LOCNET. -
Start training.
python train_RK.py
- Tensorboard log will be stored in
./pt_log. - Checkpoints and validation results will be saved in
./AdaTarget/.
@InProceedings{jo2021adatarget,
author = {Jo, Younghyun and Oh, Seoung Wug and Vajda, Peter and Kim, Seon Joo},
title = {Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}