Maximum likelihood image reconstruction from Fourier-offset data using the expectation-maximization algorithm

Author

Jennison, Brian K. ; Allebach, Jan P.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

fYear

1991

fDate

14-17 Apr 1991

Firstpage

2597

Abstract

A maximum likelihood (ML) estimate of the magnitude of a complex-valued image from measurements of its Fourier transform in a limited region offset from the origin is derived under the assumption of independent, uniformly distributed image phase samples. The expectation-maximization (EM) algorithm is employed to solve the resulting nonlinear maximum likelihood equation, and it yields a computationally efficient iterative estimator. Reconstructions from this algorithm contain significantly more energy than conventional reconstructions, but with only slightly improved reconstruction quality

Keywords

fast Fourier transforms; iterative methods; picture processing; DFT; Fourier transform; Fourier-offset data; complex-valued image; expectation-maximization algorithm; image phase samples; image quality; image reconstruction; independent uniform distribution; iterative estimator; maximum likelihood estimation; measurements; nonlinear maximum likelihood equation; reconstruction quality; Data engineering; Discrete Fourier transforms; Expectation-maximization algorithms; Fourier transforms; Image reconstruction; Iterative algorithms; Maximum likelihood estimation; Radar antennas; Reconstruction algorithms; Reflectivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Conference_Location

Toronto, Ont.

ISSN

1520-6149

Print_ISBN

0-7803-0003-3

Type

conf

DOI

10.1109/ICASSP.1991.150933

Filename

150933