Replication Data for: Super-resolution reconstruction using deep learning: should we go deeper?
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Convolutional neural networks","Deep learning","Image processing","Super-resolution reconstruction"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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)
Convolutional neural networks; Deep learning; Image processing; Super-resolution reconstruction