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Authors: Michal Kawulok 1 ; Daniel Kostrzewa 1 ; Pawel Benecki 1 and Lukasz Skonieczny 2

Affiliations: 1 Future Processing and Silesian University of Technology, Poland ; 2 Future Processing, Poland

ISBN: 978-989-758-275-2

Keyword(s): Genetic Algorithm, Image Processing, Super-resolution Reconstruction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Vision and Perception

Abstract: Super-resolution reconstruction (SRR) is aimed at increasing spatial resolution given a single image or multiple images presenting the same scene. The existing methods are underpinned with a premise that the observed low resolution images are obtained from a hypothetic high resolution image by applying a certain imaging model (IM) which degrades the image and decreases its resolution. Hence, the reconstruction consists in applying an inverse IM to recover the high resolution data. Such an approach has been found effective, if the IM is known and controlled, in particular when the low resolution images are indeed obtained from a high resolution one. However, in a real-world scenario, when SRR is performed from images originally captured at low resolution, finding appropriate IM and tuning its hyperparameters is a challenging task. In this paper, we propose to optimize the SRR hyperparameters using a genetic algorithm, which has not been reported in the literature so far. We ar gue that this may substantially improve the capacities of learning the relation between low and high resolution images. Our initial, yet highly encouraging, experimental results reported in the paper allow us to outline our research pathways to deploy the developed techniques in practice. (More)

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Paper citation in several formats:
Kawulok M., Kostrzewa D., Benecki P. and Skonieczny L. (2018). Optimizing Super-resolution Reconstruction using a Genetic Algorithm.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 599-605. DOI: 10.5220/0006654305990605

@conference{icaart18,
author={Michal Kawulok and Daniel Kostrzewa and Pawel Benecki and Lukasz Skonieczny},
title={Optimizing Super-resolution Reconstruction using a Genetic Algorithm},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={599-605},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006654305990605},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Optimizing Super-resolution Reconstruction using a Genetic Algorithm
SN - 978-989-758-275-2
AU - Kawulok M.
AU - Kostrzewa D.
AU - Benecki P.
AU - Skonieczny L.
PY - 2018
SP - 599
EP - 605
DO - 10.5220/0006654305990605

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