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Kernel-Modeling-Super-Resolution

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Official Implementation for Kernel Modeling Super-Resolution on Real Low-Resolution Images

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# Kernel Modeling Super-Resolution on Real Low-Resolution Images
#### [Project Page](https://ivrlwww.epfl.ch/ruofan/project_KMSR/KMSR.html) | [Paper](ICCV_blindSR.pdf) | [Supplementary Material](https://ivrlwww.epfl.ch/ruofan/project_KMSR/supplementary_material/0_index.html) | [Psychovisual Experiment](https://ivrlwww.epfl.ch/ruofan/exp/index.html)
by [Ruofan Zhou](https://ivrl.epfl.ch/people/Zhou), [Sabine Süsstrunk](https://ivrl.epfl.ch/people/susstrunk)

## Dependencies
- Pytorch >= 0.4.0
- OpenCV
- NVIDIA GPU
- HDF5 (only for training)
- MATLAB （only for training)

## Quick start (Demo)
In `test_code` folder, run the following command: 
```
python demo.py
```
## Training the network yourself
#### Step 1: prepare the dataset
Download [DEPD dataset](people.ee.ethz.ch/~ihnatova/index.html), prepare the patches and run `training_code/kernel_estimation/getkernels.m` in MATLAB.

#### Step 2: train a GAN on kernels
run `training_code/kernel_generator/train.py`.

#### Step 3: generate
run `training_code/kernel_generator/generate.py`.

#### Step 4: train the super-resolution network
run `training_code/super-resolution/main.py`.

## Citations
```
@inproceedings{zhou2019kernel,
  title={Kernel Modeling Super-Resolution on Real Low-Resolution Images},
  author={Zhou, Ruofan and S{\"u}sstrunk, Sabine},
  booktitle={2019 International Conference on Computer Vision},
  year={2019}
}
```

Kernel-Modeling-Super-Resolution

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