Image restoration by complexity regularization via dynamic programming

Author

Yau, Sze Fong ; Bresler, Yoram

Author_Institution

Illinois Univ., Urbana, IL, USA

Volume

3

fYear

1992

fDate

23-26 Mar 1992

Firstpage

305

Abstract

The restoration of an image modeled by piecewise-constant polygonal patches from its blurred (bandlimited) and noise corrupted version is considered. Under this model, the line-integral projections of the data image are piecewise linear signals, blurred and corrupted by noise. The break points and the associated amplitude parameters of each projection are estimated by minimizing the 1-D stochastic complexity of the projection using a recently proposed dynamic programming technique. The final image is reconstructed by convolution backprojection

Keywords

computational complexity; dynamic programming; image reconstruction; piecewise-linear techniques; 1-D stochastic complexity; amplitude parameters; blurred image; break points; complexity regularization; convolution backprojection; dynamic programming; image restoration; line-integral projections; noisy image; piecewise linear signals; piecewise-constant polygonal patches; Amplitude estimation; Convolution; Dynamic programming; Image reconstruction; Image restoration; Magnetic resonance imaging; Piecewise linear techniques; Signal restoration; Smoothing methods; Stochastic resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226240

Filename

226240