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Review of Image Similarity Measures for Joint Image Reconstruction from Multiple Measurements

In: Time-dependent Problems in Imaging and Parameter Identification

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  • Ming Jiang

    (Peking University, School of Mathematical Sciences)

Abstract

It is fundamental in image processing how to measure image similarity quantitatively for tasks such as image quality assessment, image registration, image reconstruction from multiple measurements, etc.. An image similarity measure (ISM) is both task-dependent and feature-dependent, and must be designed according to the characteristics of specific tasks and features. Simply applying distances from the mathematical metric theory or general divergences to spaces of images or spaces of image features usually does not provide appropriate ISMs. In this chapter, we review several ISMs for image reconstruction problems from multiple measurements of various types in recent work. The multiple measurements considered here include multi-modality, multi-spectral, and multi-temporal measurements, with multi-modality tomography, multi-spectral XCT, and dynamic tomography, as the imaging applications, respectively. We focus on motivations and constructions of the ISMs and avoid their general rigorous mathematical presentations to simplify notations for the readability for a general audience. ISMs under review are proposed for image structural similarity and have been successfully applied to image reconstruction from multiple measurements.

Suggested Citation

  • Ming Jiang, 2021. "Review of Image Similarity Measures for Joint Image Reconstruction from Multiple Measurements," Springer Books, in: Barbara Kaltenbacher & Thomas Schuster & Anne Wald (ed.), Time-dependent Problems in Imaging and Parameter Identification, pages 267-286, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-57784-1_9
    DOI: 10.1007/978-3-030-57784-1_9
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