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1 January 2011 Higher order singular value decomposition of tensors for fusion of registered images
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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.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Michael G. Thomason and Jens Gregor "Higher order singular value decomposition of tensors for fusion of registered images," Journal of Electronic Imaging 20(1), 013023 (1 January 2011).
Published: 1 January 2011


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