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.
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