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Journal of Electronic Imaging

Higher order singular value decomposition of tensors for fusion of registered images
Author(s): Michael G. Thomason; Jens Gregor
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Paper Abstract

This paper describes a computational method using tensor math for higher order singular value decomposition (HOSVD) of registered images. Tensor decomposition is a rigorous way to expose structure embedded in multidimensional datasets. Given a dataset of registered 2-D images, the dataset is represented in tensor format and HOSVD of the tensor is computed to obtain a set of 2-D basis images. The basis images constitute a linear decomposition of the original dataset. HOSVD is data-driven and does not require the user to select parameters or assign thresholds. A specific application uses the basis images for pixel-level fusion of registered images into a single image for visualization. The fusion is optimized with respect to a measure of mean squared error. HOSVD and image fusion are illustrated empirically with four real datasets: (1) visible and infrared data of a natural scene, (2) MRI and x ray CT brain images, and in nondestructive testing (3) x ray, ultrasound, and eddy current images, and (4) x ray, ultrasound, and shearography images.

Paper Details

Date Published: 1 January 2011
PDF: 9 pages
J. Electron. Imaging. 20(1) 013023 doi: 10.1117/1.3563592
Published in: Journal of Electronic Imaging Volume 20, Issue 1
Show Author Affiliations
Michael G. Thomason, The Univ. of Tennessee (United States)
Jens Gregor, The Univ. of Tennessee (United States)


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