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Fast algorithms for phase diversity-based blind deconvolution
被引：42
作者：
Vogel, CR
[1
]
Chan, T
[1
]
Plemmons, R
[1
]
机构：
[1] Montana State Univ, Dept Math Sci, Bozeman, MT 59717 USA
来源：
ADAPTIVE OPTICAL SYSTEM TECHNOLOGIES, PARTS 1 AND 2
|
1998年
/
3353卷
关键词：
phase diversity;
blind deconvolution;
phase retrieval;
quasi-Newton methods;
D O I：
10.1117/12.321720
中图分类号：
O43 [光学];
学科分类号：
070207 ;
0803 ;
摘要：
Phase diversity is a technique for obtaining estimates of both the object and the phase, by exploiting the simultaneous collection of two (or more) short-exposure optical images, one of which has been formed by further blurring the conventional image in some known fashion. This paper concerns a fast computational algorithm based upon a regularized variant of the Gauss-Newton optimization method for phase diversity-based estimation when a Gaussian likelihood fit-to-data criterion is applied. Simulation studies are provided to demonstrate that the method is remarkably robust and numerically efficient.
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页码：994 / 1005
页数：12
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