This is official pytorch implementation of the paper "RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars"
- Python=3.7
- PyTorch
- opencv-python
- scikit-image(skimage)
Clone this repository into any place you want.
git clone https://github.com/junsang7777/RZSR.git
cd RZSR
You can test our SR algorithm with your images. Place your image in "set" folder. (img - RGB, dep - Depth, ker - Kernel)
If you don't have kernel files, Change "kernels = None" in 'main.py' script
You can get the depth & kernel of the images from the repository as follows: Depth : monodepth2 & Adabins Kernel : KernelGAN
python main.py
Set dir : img (Random gaussian blurred img) , dep (Adabins depth estimation result) , ker ( KernelGAN estimation result)
hyper-parmeter : hitogram_threshold & Number of BINs & configs.py etc...
“Zero-Shot” Super-Resolution using Deep Internal Learning | git
Robust Reference-based Super-Resolution with Similarity-Aware Deformable Convolution
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining | git
The code is based on pytorch-ZSSR
@ARTICLE{9868165,
author={Yoo, Jun-Sang and Kim, Dong-Wook and Lu, Yucheng and Jung, Seung-Won},
journal={IEEE Transactions on Multimedia},
title={RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars},
year={2022},
volume={},
number={},
pages={1-13},
doi={10.1109/TMM.2022.3202018}}