Histogram derived penalty functions in gradient-based optical tomography

Andreas Hielscher, Sebastian Bartel

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.
    Original languageEnglish (US)
    Title of host publicationBiomedical Optical Spectroscopy and Diagnostics, T. Li, ed., Vol. 38 of OSA Trends in Optics and Photonics (Optica Publishing Group, 2000)
    StatePublished - 2000

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