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Article

Enhancement of Component Images of Multispectral Data by Denoising with Reference

1
Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine
2
Engineering School of Applied Sciences and Technology, University of Rennes 1, 22305 Lannion, France
3
Computational Imaging Group, Tampere University, Tampere 33720, Finland
*
Author to whom correspondence should be addressed.
Received: 12 January 2019 / Revised: 25 February 2019 / Accepted: 10 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Multispectral Image Acquisition, Processing and Analysis)
Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach. View Full-Text
Keywords: remote sensing; multispectral imaging; DCT-filtering; vectorial (three-dimensional) filtering; BM3D-filtering; filtering with reference remote sensing; multispectral imaging; DCT-filtering; vectorial (three-dimensional) filtering; BM3D-filtering; filtering with reference
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MDPI and ACS Style

Abramov, S.; Uss, M.; Lukin, V.; Vozel, B.; Chehdi, K.; Egiazarian, K. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sens. 2019, 11, 611. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060611

AMA Style

Abramov S, Uss M, Lukin V, Vozel B, Chehdi K, Egiazarian K. Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sensing. 2019; 11(6):611. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060611

Chicago/Turabian Style

Abramov, Sergey, Mikhail Uss, Vladimir Lukin, Benoit Vozel, Kacem Chehdi, and Karen Egiazarian. 2019. "Enhancement of Component Images of Multispectral Data by Denoising with Reference" Remote Sensing 11, no. 6: 611. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060611

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