A computational method for the restoration of images with an unknown, spatially-varying blur | Zendy

Johnathan M. Bardsley | Zendy; S. M. Jefferies | Zendy; James G. Nagy | Zendy; Robert J. Plemmons | Zendy

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A computational method for the restoration of images with an unknown, spatially-varying blur

Author(s) -

Johnathan M. Bardsley,

S. M. Jefferies,

James G. Nagy,

Robert J. Plemmons

Publication year - 2006

Publication title -

optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.14.001767

Subject(s) - image restoration , artificial intelligence , computer science , computer vision , invariant (physics) , image processing , image (mathematics) , algorithm , optics , mathematics , physics , mathematical physics

In this paper, we present an algorithm for the restoration of images with an unknown, spatially-varying blur. Existing computational methods for image restoration require the assumption that the blur is known and/or spatially-invariant. Our algorithm uses a combination of techniques. First, we section the image, and then treat the sections as a sequence of frames whose unknown PSFs are correlated and approximately spatially-invariant. To estimate the PSFs in each section, phase diversity is used. With the PSF estimates in hand, we then use a technique by Nagy and O'Leary for the restoration of images with a known, spatially-varying blur to restore the image globally. Test results on star cluster data are presented.

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