Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise
Image degradation by blurring is a well-known phenomenon often described by the mathematical operation of convolution. Fourier methods are well developed for recovery, or restoration, of the true image from an observed image affected by convolution blur and additive constant variance Gaussian noise....
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Veröffentlicht in: | Computational statistics & data analysis 1998-02, Vol.26 (4), p.393-410 |
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description | Image degradation by blurring is a well-known phenomenon often described by the mathematical operation of convolution. Fourier methods are well developed for recovery, or restoration, of the true image from an observed image affected by convolution blur and additive constant variance Gaussian noise. One focus of this paper is to describe another statistical restoration method which is available when the image data exhibits Poisson variability. This is a common situation when counts of recorded activity form the image, as in medical imaging contexts.
We apply Maximum Likelihood (ML) and Maximum Penalized Likelihood (MPL) procedures to deconvolve image data which has been degraded by blurring and Poisson variability in recorded activity.
A second focus is formulation and comparison of automated selection procedures for regularization (smoothing) parameters in this context. |
doi_str_mv | 10.1016/S0167-9473(97)00041-8 |
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We apply Maximum Likelihood (ML) and Maximum Penalized Likelihood (MPL) procedures to deconvolve image data which has been degraded by blurring and Poisson variability in recorded activity.
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We apply Maximum Likelihood (ML) and Maximum Penalized Likelihood (MPL) procedures to deconvolve image data which has been degraded by blurring and Poisson variability in recorded activity.
A second focus is formulation and comparison of automated selection procedures for regularization (smoothing) parameters in this context.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Cross-validation</subject><subject>Deconvolution</subject><subject>Exact sciences and technology</subject><subject>Fast computation</subject><subject>Mathematics</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Numerical approximation</subject><subject>Numerical methods in mathematical programming</subject><subject>Numerical methods in mathematical programming, optimization and calculus of variations</subject><subject>Numerical methods in optimization and calculus of variations</subject><subject>Numerical methods in probability and statistics</subject><subject>Parametric inference</subject><subject>Pattern recognition. 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Computational geometry</subject><subject>Probability and statistics</subject><subject>Regularization</subject><subject>Sciences and techniques of general use</subject><subject>Statistical algorithms</subject><subject>Statistics</subject><issn>0167-9473</issn><issn>1872-7352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFkEtv1DAUhSMEEkPhJyB5gRAsAn4ltlcIVbyqolYC1pZr33RcEjvYyaj999zOVLPt4ty7-c59nKZ5zegHRln_8RcW1RqpxDuj3lNKJWv1k2bDtOKtEh1_2myOyPPmRa03CHGp9Ka5_elu47ROZIx_YYzbnAMpUJdc3BJzIi4F4rc5eiB5IHXKednGdE1mV9wECxQSEwngc9rlcd1bkIuTuwYS3OJIXa9uwC9kyeQyx1oRSNjhZfNscGOFVw_9pPnz9cvv0-_t-cW3H6efz1svhVza0HPGAzAZhqD6AQzreu-0YlQLkGAGadyV11J0gWqtPfVcaSa0oWoQbKDipHl7mDuX_G_Fz-wUq4dxdAnyWi3ve0UZ6xHsDqAvudYCg50L_lHuLKP2Pme7z9neh2iNsvucrUbf2cFXYAZ_NAGAr8ElZ3dWON5juUMxYzS2iJKoGSWMsBJ3bJcJh715uNZV78ahuORjPQ7lnOqul4h9OmCAye0iFFt9hOQhxIJh25DjI1f_ByxsrKg</recordid><startdate>19980206</startdate><enddate>19980206</enddate><creator>Hudson, H.Malcolm</creator><creator>Lee, Thomas C.M.</creator><general>Elsevier B.V</general><general>Elsevier Science</general><general>Elsevier</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19980206</creationdate><title>Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise</title><author>Hudson, H.Malcolm ; Lee, Thomas C.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-d6212de14dfd76fe9156ca871083e4e9f49abc8435d0888c0c278138907f31f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Cross-validation</topic><topic>Deconvolution</topic><topic>Exact sciences and technology</topic><topic>Fast computation</topic><topic>Mathematics</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Numerical approximation</topic><topic>Numerical methods in mathematical programming</topic><topic>Numerical methods in mathematical programming, optimization and calculus of variations</topic><topic>Numerical methods in optimization and calculus of variations</topic><topic>Numerical methods in probability and statistics</topic><topic>Parametric inference</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Probability and statistics</topic><topic>Regularization</topic><topic>Sciences and techniques of general use</topic><topic>Statistical algorithms</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hudson, H.Malcolm</creatorcontrib><creatorcontrib>Lee, Thomas C.M.</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computational statistics & data analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hudson, H.Malcolm</au><au>Lee, Thomas C.M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise</atitle><jtitle>Computational statistics & data analysis</jtitle><date>1998-02-06</date><risdate>1998</risdate><volume>26</volume><issue>4</issue><spage>393</spage><epage>410</epage><pages>393-410</pages><issn>0167-9473</issn><eissn>1872-7352</eissn><abstract>Image degradation by blurring is a well-known phenomenon often described by the mathematical operation of convolution. Fourier methods are well developed for recovery, or restoration, of the true image from an observed image affected by convolution blur and additive constant variance Gaussian noise. One focus of this paper is to describe another statistical restoration method which is available when the image data exhibits Poisson variability. This is a common situation when counts of recorded activity form the image, as in medical imaging contexts.
We apply Maximum Likelihood (ML) and Maximum Penalized Likelihood (MPL) procedures to deconvolve image data which has been degraded by blurring and Poisson variability in recorded activity.
A second focus is formulation and comparison of automated selection procedures for regularization (smoothing) parameters in this context.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0167-9473(97)00041-8</doi><tpages>18</tpages></addata></record> |
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subjects | Applied sciences Artificial intelligence Computer science control theory systems Cross-validation Deconvolution Exact sciences and technology Fast computation Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical approximation Numerical methods in mathematical programming Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Numerical methods in probability and statistics Parametric inference Pattern recognition. Digital image processing. Computational geometry Probability and statistics Regularization Sciences and techniques of general use Statistical algorithms Statistics |
title | Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise |
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