This repository contains the implementation of the paper

F. Schirrmacher, B. Lorch, B. Stimpel, T. Köhler and, C. Riess, "SR²: Super-Resolution With Structure-Aware Reconstruction," in IEEE International Conference on Image Processing (ICIP), pp. 533-537, 2020, doi: 10.1109/ICIP40778.2020.9191253. IEEE

If you use this code in your work, please cite:

    @INPROCEEDINGS{9191253,
        author={F. {Schirrmacher} and B. {Lorch} and B. {Stimpel} and T. {Köhler} and C. {Riess}},
        booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, 
        title={SR2: Super-Resolution With Structure-Aware Reconstruction},
        year={2020},
        pages={533--537},
        doi={10.1109/ICIP40778.2020.9191253}}

To download the code, fork the repository or clone it using the following command:

  git clone https://github.com/franziska-schirrmacher/sr2.git

• python 3.7
• keras 2.3.1
• tensorflow 1.14
• h5py 2.10.0

• checkpoints: This folder contains the stored weights (needs to be created)

• data: This folder contains all datasets for each of the experiments (needs to be created)

• src: This folder contains the source code of the experiments and the proposed architectur

In order to reproduce the results, you need to download the MNIST and the SVHN datset. In the Train.py file uncomment line #22 to store the results the first time you use the dataset