Replication Data for: On training deep networks for satellite image super-resolution
<|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 IGARSS 2019 paper: M. Kawulok, S. Piechaczek, K. Hrynczenko, P. Benecki, D. Kostrzewa, J. Nalepa: 'On training deep networks for satellite image super-resolution,' in Proc. IGARSS 2019. The data are split into training and validation sets as described in Table 1 in our paper. Low-resolution patches have been obtained from high-resolution ones following 8 different scenarios - all of them are included in the dataset.The dataset is split into three files due to technical reasons: (i) DIV2K high-resolution patches, (ii) DIV2K low-resolution patches (8 versions), and (iii) Sentinel-2 patches (low- and high-resolution)' . 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 IGARSS 2019 paper: M. Kawulok, S. Piechaczek, K. Hrynczenko, P. Benecki, D. Kostrzewa, J. Nalepa: 'On training deep networks for satellite image super-resolution,' in Proc. IGARSS 2019. The data are split into training and validation sets as described in Table 1 in our paper. Low-resolution patches have been obtained from high-resolution ones following 8 different scenarios - all of them are included in the dataset.The dataset is split into three files due to technical reasons: (i) DIV2K high-resolution patches, (ii) DIV2K low-resolution patches (8 versions), and (iii) Sentinel-2 patches (low- and high-resolution)' . 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 IGARSS 2019 paper: M. Kawulok, S. Piechaczek, K. Hrynczenko, P. Benecki, D. Kostrzewa, J. Nalepa: 'On training deep networks for satellite image super-resolution,' in Proc. IGARSS 2019. The data are split into training and validation sets as described in Table 1 in our paper. Low-resolution patches have been obtained from high-resolution ones following 8 different scenarios - all of them are included in the dataset.The dataset is split into three files due to technical reasons: (i) DIV2K high-resolution patches, (ii) DIV2K low-resolution patches (8 versions), and (iii) Sentinel-2 patches (low- and high-resolution)
Convolutional neural networks; Deep learning; Image processing; Super-resolution reconstruction