Maximum cross-entropy generalized series reconstruction

C. P. Hess, Zhi-Pei Liang, A. G. Webb, P. C. Lauterbur

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


This paper addresses the classical image reconstruction problem from limited Fourier data. Here, we assume that a high-resolution reference which provides an initial estimate of the desired image is available. A new algorithm is described which represents the desired image using a family of basis functions derived from the reference image. The selection of the most efficient basis function set from this family is guided by the principle of maximum cross-entropy. Simulation and experimental results have shown that the algorithm can achieve high resolution with a small number of data points and can also account for relative rotation and translation between the reference and the measured data.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE Comp Soc
Number of pages5
StatePublished - 1998
EventProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA
Duration: Oct 4 1998Oct 7 1998


OtherProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3)
CityChicago, IL, USA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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