Original title: Impact Of Loss Function On Multi-Frame Super-Resolution
Authors: Mezina, Anzhelika
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Nowadays, one of the most popular topics in image processing is super-resolution. Thisproblem is getting more actual even in security, since monitoring cameras are everywhere and inthe case of an incident, it is necessary to recognize a person from records. A lot of approaches exist,which are able to reconstruct image, and the most of them are based on deep learning. The main focusof this work is to analyze, which loss function for neural networks is more effective for real-worldimage reconstruction. For this experiment chosen architecture and dataset are used for multi-framesuper-resolution for _x0002_8 scaling.
Keywords: deep learning; image processing; loss function; super-resolution
Host item entry: Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers, ISBN 978-80-214-5942-7

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200703

Permalink: http://www.nusl.cz/ntk/nusl-447748


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Conference materials > Papers