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Proceedings Paper

Image denoising and deblurring using multispectral data
Author(s): E. A. Semenishchev; V. V. Voronin; V. I. Marchuk
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Paper Abstract

Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.

Paper Details

Date Published: 23 May 2017
PDF: 6 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101981G (23 May 2017); doi: 10.1117/12.2262510
Show Author Affiliations
E. A. Semenishchev, Don State Technical Univ. (Russian Federation)
V. V. Voronin, Don State Technical Univ. (Russian Federation)
V. I. Marchuk, Don State Technical Univ. (Russian Federation)

Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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