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An iterative deconvolution algorithm using combined regularization for low-order corrected astronomical images
Hualin Chen; Xiangyan Yuan; Xiangqun Cui
2008-07-10
Source PublicationAdaptive Optics Systems
Volume7015
Issue7015
PagesVol.7015 2F-1-12
Conference Date2008-6-23
Conference PlaceMarseille, France
Funding OrganizationThe International Society for Optical Engineering
PublisherSPIE
Abstract
An iterative deconvolution algorithm is presented in detail which utilizes regularization to combine maximum-likelihood (ML) estimate of convolution error and several physical constraints to build error function. The physical constraints used in this algorithm include positivity, band-limit information and the information of multiple frames. By minimizing the combined error metric of individual ones, the object can be expected to be recovered from the noisy data. In addition, numerical simulation of Phase Screen distorted by atmospheric turbulence following the Kolmogorov spectrum is also made to generate the PSFs which are
used to simulate the degraded images.
KeywordAdaptive Optics Iterative Deconvolution Maximum-likelihood Physical Constraints
Subject Area自适应光学
Document Type会议论文
Identifierhttp://ir.niaot.ac.cn/handle/114a32/542
Collection会议论文
Recommended Citation
GB/T 7714
Hualin Chen,Xiangyan Yuan,Xiangqun Cui. An iterative deconvolution algorithm using combined regularization for low-order corrected astronomical images[C]:SPIE,2008:Vol.7015 2F-1-12.
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