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Authors: Purbaditya Bhattacharya 1 ; Jörg Riechen 2 and Udo Zölzer 1

Affiliations: 1 Helmut Schmidt University, Germany ; 2 WTD 71 Military Service Center for Ships, Naval Weapons, Maritime Technology and Research and Eckernförde, Germany

Keyword(s): Image Processing, Convolutional Neural Network, Denoising, Super-resolution.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

Abstract: Image enhancement approach with Convolutional Neural Network (CNN) for infrared (IR) images from maritime environment, is proposed in this paper. The approach includes different CNNs to improve the resolution and to reduce noise artefacts in maritime IR images. The denoising CNN employs a residual architecture which is trained to reduce graininess and fixed pattern noise. The super-resolution CNN employs a similar architecture to learn the mapping from a low-resolution to multi-scale high-resolution images. The performance of the CNNs is evaluated on the IR test dataset with standard evaluation methods and the evaluation results show an overall improvement in the quality of the IR images.

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Paper citation in several formats:
Bhattacharya, P.; Riechen, J. and Zölzer, U. (2018). Infrared Image Enhancement in Maritime Environment with Convolutional Neural Networks. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 37-46. DOI: 10.5220/0006618700370046

@conference{visapp18,
author={Purbaditya Bhattacharya. and Jörg Riechen. and Udo Zölzer.},
title={Infrared Image Enhancement in Maritime Environment with Convolutional Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={37-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006618700370046},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Infrared Image Enhancement in Maritime Environment with Convolutional Neural Networks
SN - 978-989-758-290-5
IS - 2184-4321
AU - Bhattacharya, P.
AU - Riechen, J.
AU - Zölzer, U.
PY - 2018
SP - 37
EP - 46
DO - 10.5220/0006618700370046
PB - SciTePress

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