@inproceedings{4a566c8719014d0e9e6e232b66b763f3,
title = "Supervised Deep Kriging for Single-Image Super-Resolution",
abstract = "We propose a novel single-image super-resolution approach based on the geostatistical method of kriging. Kriging is a zero-bias minimum-variance estimator that performs spatial interpolation based on a weighted average of known observations. Rather than solving for the kriging weights via the traditional method of inverting covariance matrices, we propose a supervised form in which we learn a deep network to generate said weights. We combine the kriging weight generation and kriging process into a joint network that can be learned end-to-end. Our network achieves competitive super-resolution results as other state-of-the-art methods. In addition, since the super-resolution process follows a known statistical framework, we are able to estimate bias and variance, something which is rarely possible for other deep networks.",
author = "Gianni Franchi and Angela Yao and Andreas Kolb",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 40th German Conference on Pattern Recognition, GCPR 2018 ; Conference date: 09-10-2018 Through 12-10-2018",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-12939-2\_44",
language = "English",
isbn = "9783030129385",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "638--649",
editor = "Thomas Brox and Andr{\'e}s Bruhn and Mario Fritz",
booktitle = "Pattern Recognition - 40th German Conference, GCPR 2018, Proceedings",
}