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2018 | OriginalPaper | Buchkapitel

Towards Evolutionary Super-Resolution

verfasst von : Michal Kawulok, Pawel Benecki, Daniel Kostrzewa, Lukasz Skonieczny

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

Super-resolution reconstruction (SRR) allows for producing a high-resolution (HR) image from a set of low-resolution (LR) observations. The majority of existing methods require tuning a number of hyper-parameters which control the reconstruction process and configure the imaging model that is supposed to reflect the relation between high and low resolution. In this paper, we demonstrate that the reconstruction process is very sensitive to the actual relation between LR and HR images, and we argue that this is a substantial obstacle in deploying SRR in practice. We propose to search the hyper-parameter space using a genetic algorithm (GA), thus adapting to the actual relation between LR and HR, which has not been reported in the literature so far. The results of our extensive experimental study clearly indicate that our GA improves the capacities of SRR. Importantly, the GA converges to different values of the hyper-parameters depending on the applied degradation procedure, which is confirmed using statistical tests.

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Vorheriges Kapitel Can the Relevance Index be Used to Evolve Relevant Feature Sets?
Nächstes Kapitel Evolvable Deep Features
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Metadaten
Titel
Towards Evolutionary Super-Resolution
verfasst von
Michal Kawulok
Pawel Benecki
Daniel Kostrzewa
Lukasz Skonieczny
Copyright-Jahr
2018
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-77537-1

Electronic ISBN: 978-3-319-77538-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77538-8_33

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