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Ricci-Notation Tensor Framework for Model-based Approaches to Imaging
  DOI :  10.2352/J.ImagingSci.Technol.2024.68.4.040504
Abstract
Abstract

Model-based approaches to imaging, such as specialized image enhancements in astronomy, facilitate explanations of relationships between observed inputs and computed outputs. These models may be expressed with extended matrix-vector (EMV) algebra, especially when they involve only scalars, vectors, and matrices, and with n-mode or index notations, when they involve multidimensional arrays, also called numeric tensors or, simply, tensors. Although this paper features an example, inspired by exoplanet imaging, that employs tensors to reveal (inverse) 2D fast Fourier transforms in an image enhancement model, the work is actually about the tensor algebra and software, or tensor frameworks, available for model-based imaging. The paper proposes a Ricci-notation tensor (RT) framework, comprising a dual-variant index notation, with Einstein summation convention, and codesigned object-oriented software, called the RTToolbox for MATLAB. Extensions to Ricci notation offer novel representations for entrywise, pagewise, and broadcasting operations popular in EMV frameworks for imaging. Complementing the EMV algebra computable with MATLAB, the RTToolbox demonstrates programmatic and computational efficiency via careful design of numeric tensor and dual-variant index classes. Compared to its closest competitor, also a numeric tensor framework that uses index notation, the RT framework enables superior ways to model imaging problems and, thereby, to develop solutions.

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  Cite this article 

Dileepan Joseph, "Ricci-Notation Tensor Framework for Model-based Approaches to Imagingin Journal of Imaging Science and Technology,  2024,  pp 1 - 17,  https://doi.org/10.2352/J.ImagingSci.Technol.2024.68.4.040504

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Copyright © Society for Imaging Science and Technology 2024
  Article timeline 
  • received May 2023
  • accepted January 2024

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