Abstract

We propose a new algorithm for restoring quantum-limited images. The algorithm is based on projection methods but uses constraint sets in which set membership is based on probabilistic measures. Such constraints can be regarded as soft as opposed to hard constraints in which less latitude is given in defining set membership. We show that the restoration of quantum-limited images has certain similarities to estimating a probability-density function. We apply the algorithm to a widely used image phantom and demonstrate that the processed image features less noise without blurred edges.

© 1995 Optical Society of America

Convex projections algorithm for restoration of limited-angle chromotomographic images

Andrzej K. Brodzik and Jonathan M. Mooney
J. Opt. Soc. Am. A 16(2) 246-257 (1999)

Projection-based image restoration

Henry Stark and Elwood T. Olsen
J. Opt. Soc. Am. A 9(11) 1914-1919 (1992)

Image restoration by convex projections in the presence of noise

M. I. Sezan and Henry Stark
Appl. Opt. 22(18) 2781-2789 (1983)

Image restoration by the method of generalized projections with application to restoration from magnitude

Aharon Levi and Henry Stark
J. Opt. Soc. Am. A 1(9) 932-943 (1984)

Image restoration in singular vector space by the method of convex projections

Osami Sasaki and Taku Yamagami
Appl. Opt. 26(7) 1216-1221 (1987)