Super-Resolution Reconstruction Using Deep Learning: Should We Go Deeper?-Reference-Cited by-同舟云学术

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Super-Resolution Reconstruction Using Deep Learning: Should We Go Deeper?

Published:2019 Issue: Volume: Page:204-216
ISSN:1865-0929
Container-title:Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis
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Author:

Kostrzewa Daniel^ORCID,Piechaczek Szymon,Hrynczenko Krzysztof,Benecki Paweł^ORCID,Nalepa Jakub^ORCID,Kawulok Michal^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-19093-4_16

Reference42 articles.

1. Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

2. Benecki, P., Kawulok, M., Kostrzewa, D., Skonieczny, L.: Evaluating super-resolution reconstruction of satellite images. Acta Astronaut. 153, 15–25 (2018)

3. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

4. Berkeley Segmentation Dataset: 01 December 2019. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

5. Bevilacqua, M., Roumy, A., Guillemot, C., Alberi Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp. 135.1–135.10. BMVA Press (2012)

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