Presentation 2005/6/16
Probablistic Inference to the Problem of Phase Retrieval in Adaptive Optics
Yohei SAIKA, Hidetoshi NISHIMORI,
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Abstract(in English) We formulate the problem of phase retrieval in adaptive optics using the Markov-random field model based on the maximizer of the posterior marginal (MPM) estimate. The property of our method is to retrieve wave-fronts precisely using the model prior selected due to the sensitivity of measurement by optical instruments. Then, the Monte Carlo simulation for the typical wave-front in adaptive optics shows the results that the MPM estimate works better than the conventional least-squares estimation if we appropriately set hyperparameters, and that the performance of our method is improved using the model prior with interactions enhansing smooth structures between neighboring sites and the uniform field. We also show that these results are qualitatively confirmed by the replica theory established in the theory of spin glasses.
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Keyword(in English) adaptive optics / phase retrieval / Markov-random field model / the maximizer of the posterior marginal estimate
Paper # NC2005-12
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Committee NC
Conference Date 2005/6/16(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Probablistic Inference to the Problem of Phase Retrieval in Adaptive Optics
Sub Title (in English)
Keyword(1) adaptive optics
Keyword(2) phase retrieval
Keyword(3) Markov-random field model
Keyword(4) the maximizer of the posterior marginal estimate
1st Author's Name Yohei SAIKA
1st Author's Affiliation Department of Electrical and Information Engineering, Wakayama National College of Technology()
2nd Author's Name Hidetoshi NISHIMORI
2nd Author's Affiliation Department of Physics, Tokyo Institute of Technology
Date 2005/6/16
Paper # NC2005-12
Volume (vol) vol.105
Number (no) 130
Page pp.pp.-
#Pages 6
Date of Issue