Replication Data for: Super-resolution reconstruction using deep learning: should we go deeper?

This dataset contains image patches used to train deep networks for super-resolution reconstruction, used for the experiments reported in our paper: D. Kostrzewa, S. Piechaczek, K. Hrynczenko, P. Benecki, J. Nalepa, and M. Kawulok: "Super-resolution reconstruction using deep learning: should we go deeper?," in Proc. BDAS 2019, Communications in Computer and Information Science, Springer, 2019. The data are split into training and validation sets, containing 12,800 and 1600 patches, respectively. Every patch is of size 224x224 pixels (high resolution), coupled with a low resolution patch (112x112 pixels). The patches were extracted from the publicly available DIV2K dataset (https://data.vision.ee.ethz.ch/cvl/DIV2K).