Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling
In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of...
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description | In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation |
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Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2006.881970</identifier><identifier>PMID: 17076398</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Adaptive filtering ; Algorithms ; Applied sciences ; Coefficients ; Computer Simulation ; Detection, estimation, filtering, equalization, prediction ; Equations ; Exact sciences and technology ; Gaussian ; generalized Gaussian (GG) modeling ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Image resolution ; Information Storage and Retrieval - methods ; Information, signal and communications theory ; Likelihood Functions ; Mathematical analysis ; Mathematical models ; maximum a posteriori (MAP) estimation ; Miscellaneous ; Models, Statistical ; Noise shaping ; Normal Distribution ; Numerical Analysis, Computer-Assisted ; Probability density function ; Probability density functions ; Radar ; Reproducibility of Results ; Sensitivity and Specificity ; Shape ; Signal and communications theory ; Signal processing ; Signal Processing, Computer-Assisted ; Signal representation. Spectral analysis ; Signal resolution ; Signal, noise ; Spatial resolution ; Speckle ; Statistics ; Studies ; Synthetic aperture radar ; synthetic aperture radar (SAR) images ; Telecommunications and information theory ; undecimated wavelet decomposition ; Wavelet ; Wavelet coefficients</subject><ispartof>IEEE transactions on image processing, 2006-11, Vol.15 (11), p.3385-3399</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-675a5e0b794fedadfc0cefc6f794268164f4cd5ccf1439812231bafd42c628fa3</citedby><cites>FETCH-LOGICAL-c405t-675a5e0b794fedadfc0cefc6f794268164f4cd5ccf1439812231bafd42c628fa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1709983$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1709983$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18214574$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17076398$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Argenti, F.</creatorcontrib><creatorcontrib>Bianchi, T.</creatorcontrib><creatorcontrib>Alparone, L.</creatorcontrib><title>Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation</description><subject>Adaptive filtering</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Coefficients</subject><subject>Computer Simulation</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Equations</subject><subject>Exact sciences and technology</subject><subject>Gaussian</subject><subject>generalized Gaussian (GG) modeling</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Image resolution</subject><subject>Information Storage and Retrieval - methods</subject><subject>Information, signal and communications theory</subject><subject>Likelihood Functions</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>maximum a posteriori (MAP) estimation</subject><subject>Miscellaneous</subject><subject>Models, Statistical</subject><subject>Noise shaping</subject><subject>Normal Distribution</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Probability density function</subject><subject>Probability density functions</subject><subject>Radar</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Shape</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal representation. Spectral analysis</subject><subject>Signal resolution</subject><subject>Signal, noise</subject><subject>Spatial resolution</subject><subject>Speckle</subject><subject>Statistics</subject><subject>Studies</subject><subject>Synthetic aperture radar</subject><subject>synthetic aperture radar (SAR) images</subject><subject>Telecommunications and information theory</subject><subject>undecimated wavelet decomposition</subject><subject>Wavelet</subject><subject>Wavelet coefficients</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNp9kc9rFDEYhoNY7A89exAkCIqX2X7JZJKZ49rqdmEXi9bzkM18KanZyTSZEdq_vll2oeLBU0K-Jw9870vIWwYzxqA5v1lezziAnNU1axS8ICesEawAEPxlvkOlCsVEc0xOU7oDYKJi8hU5ZgqULJv6hLj15EcXMQU_jS70dD2_ppeYBjS_vetvabD05_wHXW71LSb6RSfsaMZWwWjvH-i808Po_iBdYI9Re_eY5ws9peR0T4fO0nXocGd6TY6s9gnfHM4z8uvb15uLq2L1fbG8mK8KI6AaC6kqXSFsVCMsdrqzBgxaI21-4LJmUlhhusoYy0TegHFeso22neBG8trq8ox82nuHGO4nTGO7dcmg97rHMKVWNsBLXjUZ_PxfkMkcnchBQUY__IPehSn2eY22lqos6xp2vvM9ZGJIKaJth-i2Oj60DNpdW21uq9211e7byj_eH7TTZovdM3-oJwMfD4BOOW8bdW9ceuZqnhtVInPv9pxDxL81TZaUTyB8pMw</recordid><startdate>20061101</startdate><enddate>20061101</enddate><creator>Argenti, F.</creator><creator>Bianchi, T.</creator><creator>Alparone, L.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Spectral analysis</topic><topic>Signal resolution</topic><topic>Signal, noise</topic><topic>Spatial resolution</topic><topic>Speckle</topic><topic>Statistics</topic><topic>Studies</topic><topic>Synthetic aperture radar</topic><topic>synthetic aperture radar (SAR) images</topic><topic>Telecommunications and information theory</topic><topic>undecimated wavelet decomposition</topic><topic>Wavelet</topic><topic>Wavelet coefficients</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Argenti, F.</creatorcontrib><creatorcontrib>Bianchi, T.</creatorcontrib><creatorcontrib>Alparone, L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Argenti, F.</au><au>Bianchi, T.</au><au>Alparone, L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2006-11-01</date><risdate>2006</risdate><volume>15</volume><issue>11</issue><spage>3385</spage><epage>3399</epage><pages>3385-3399</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>17076398</pmid><doi>10.1109/TIP.2006.881970</doi><tpages>15</tpages></addata></record> |
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subjects | Adaptive filtering Algorithms Applied sciences Coefficients Computer Simulation Detection, estimation, filtering, equalization, prediction Equations Exact sciences and technology Gaussian generalized Gaussian (GG) modeling Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image resolution Information Storage and Retrieval - methods Information, signal and communications theory Likelihood Functions Mathematical analysis Mathematical models maximum a posteriori (MAP) estimation Miscellaneous Models, Statistical Noise shaping Normal Distribution Numerical Analysis, Computer-Assisted Probability density function Probability density functions Radar Reproducibility of Results Sensitivity and Specificity Shape Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal representation. Spectral analysis Signal resolution Signal, noise Spatial resolution Speckle Statistics Studies Synthetic aperture radar synthetic aperture radar (SAR) images Telecommunications and information theory undecimated wavelet decomposition Wavelet Wavelet coefficients |
title | Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling |
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