Fast PSF reconstruction using the frozen flow hypothesis | Zendy

Qing He Chu | Zendy; Sarah Knepper | Zendy; James G. Nagy | Zendy; S. M. Jefferies | Zendy

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Fast PSF reconstruction using the frozen flow hypothesis

Author(s) -

Qing He Chu,

Sarah Knepper,

James G. Nagy,

S. M. Jefferies

Publication year - 2011

Publication title -

imaging and applied optics

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1364/srs.2011.smc4

Subject(s) - deconvolution , frame (networking) , blind deconvolution , computer science , algorithm , point spread function , wavefront , flow (mathematics) , least squares function approximation , point (geometry) , artificial intelligence , mathematics , computer vision , optics , physics , statistics , geometry , telecommunications , estimator

For multi-frame blind deconvolution (MFBD) it is important to obtain good initial approximations of the point spread function (PSF) for each frame. Here we show that if a Taylor frozen flow hypothesis holds for short periods, then by exploiting these correlations in multiple wavefront sensor measurements, it is possible to obtain accurate estimates of the PSFs. The approach requires solving a large and sparse least squares problem.

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