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JOIG 2022 Vol.10(1): 10-16
doi: 10.18178/joig.10.1.10-16

Blind Image Restoration and Super-Resolution for Multispectral Images Using Sparse Optimization

Yoshitaka Izumi, Dan Suto, Sota Kawakami, and Hiroyuki Kudo
University of Tsukuba, Tsukuba, Japan

Abstract—The purpose of this paper is two-fold. First, we extend the Blind Image Deconvolution (BID) and blind Super Resolution (SR) methods developed in our previous work to multispectral images. Second, we introduce a new regularization technique called Patch-Based regularization in the BID and SR problems. This technique uses a low-rank property of image patches obtained by dividing each channel image as well as correlations in image intensity among different channels. We demonstrate performances of the proposed methods by simulation studies using images of a multispectral camera.

Index Terms—super-resolution, blind image deconvolution, low-rank matrix recovery

Cite: Yoshitaka Izumi, Dan Suto, Sota Kawakami, and Hiroyuki Kudo, "Blind Image Restoration and Super-Resolution for Multispectral Images Using Sparse Optimization," Journal of Image and Graphics, Vol. 10, No. 1, pp. 10-16, March 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.