Edge-preserving image denoising and estimation of discontinuous surfaces
In this paper, we are interested in the problem of estimating a discontinuous surface from noisy data. A novel procedure for this problem is proposed based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data....
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2006-07, Vol.28 (7), p.1075-1087 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Gijbels, I. Lambert, A. Qiu, P. |
| description | In this paper, we are interested in the problem of estimating a discontinuous surface from noisy data. A novel procedure for this problem is proposed based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface and preserve discontinuities at the same time. Since an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this procedure can be applied directly to image denoising. Numerical studies show that it works well in applications, compared to some existing procedures |
| doi_str_mv | 10.1109/TPAMI.2006.140 |
| format | Article |
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A novel procedure for this problem is proposed based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface and preserve discontinuities at the same time. Since an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this procedure can be applied directly to image denoising. 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Digital image processing. Computational geometry ; Preserves ; smoothing ; Smoothing methods ; Surface cleaning ; Surface fitting ; weighted residual mean square</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2006-07, Vol.28 (7), p.1075-1087</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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A novel procedure for this problem is proposed based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface and preserve discontinuities at the same time. Since an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this procedure can be applied directly to image denoising. Numerical studies show that it works well in applications, compared to some existing procedures</description><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Application software</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Bayesian methods</subject><subject>Computer science; control theory; systems</subject><subject>Computer Simulation</subject><subject>Continuity</subject><subject>Corners</subject><subject>Discontinuity</subject><subject>edges</subject><subject>Exact sciences and technology</subject><subject>Filtering</subject><subject>Image denoising</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image restoration</subject><subject>Information Storage and Retrieval - methods</subject><subject>Intelligence</subject><subject>jump-preserving estimation</subject><subject>Kernel</subject><subject>local linear fit</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Models, Statistical</subject><subject>noise</subject><subject>nonparametric regression</subject><subject>Pattern analysis</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Preserves</subject><subject>smoothing</subject><subject>Smoothing methods</subject><subject>Surface cleaning</subject><subject>Surface fitting</subject><subject>weighted residual mean square</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0c9LHDEUB_Aglrq1vXoRZCi0Pc32vfzOUcRWwdIe7DnEyZtlZDezJjtC__tm3QXFQ3sKCZ-8vLwvYycIc0RwX29_nf-4nnMAPUcJB2yGTrhWKOEO2QxQ89Zabo_Yu1LuAVAqEG_ZEWrjODgzY1eXcUHtOlOh_DikRTOswoKaSGkcynYfUmyobOrxZhhTM_ZNHEo3ps2QpnEqTZlyHzoq79mbPiwLfdivx-z3t8vbi6v25uf364vzm7aTQmxaKzplhdJKokLdaYkxcmeNkKLnJFGiBoIoNd5pklYFBVbxqiEEULEXx-zLru46jw9T7cyvaj-0XIZEtR9vnUbnjORVfv6n1BbBGqP-C7mt45UOKvz4Ct6PU071u95qZYRwSlY036Euj6Vk6v061-nlPx7BbzPzT5n5bWa-ZlYvnO2rTncris98H1IFn_YglC4s-xxSN5RnZ6wU5unl050biOhFmTpb4cRfpNWk0A</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Gijbels, I.</creator><creator>Lambert, A.</creator><creator>Qiu, P.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Digital image processing. Computational geometry</topic><topic>Preserves</topic><topic>smoothing</topic><topic>Smoothing methods</topic><topic>Surface cleaning</topic><topic>Surface fitting</topic><topic>weighted residual mean square</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gijbels, I.</creatorcontrib><creatorcontrib>Lambert, A.</creatorcontrib><creatorcontrib>Qiu, P.</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>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gijbels, I.</au><au>Lambert, A.</au><au>Qiu, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Edge-preserving image denoising and estimation of discontinuous surfaces</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2006-07-01</date><risdate>2006</risdate><volume>28</volume><issue>7</issue><spage>1075</spage><epage>1087</epage><pages>1075-1087</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>In this paper, we are interested in the problem of estimating a discontinuous surface from noisy data. A novel procedure for this problem is proposed based on local linear kernel smoothing, in which local neighborhoods are adapted to the local smoothness of the surface measured by the observed data. The procedure can therefore remove noise correctly in continuity regions of the surface and preserve discontinuities at the same time. Since an image can be regarded as a surface of the image intensity function and such a surface has discontinuities at the outlines of objects, this procedure can be applied directly to image denoising. Numerical studies show that it works well in applications, compared to some existing procedures</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>16792097</pmid><doi>10.1109/TPAMI.2006.140</doi><tpages>13</tpages></addata></record> |
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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2006-07, Vol.28 (7), p.1075-1087 |
| issn | 0162-8828 1939-3539 2160-9292 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive filters Algorithms Application software Applied sciences Artificial Intelligence Bayesian methods Computer science control theory systems Computer Simulation Continuity Corners Discontinuity edges Exact sciences and technology Filtering Image denoising Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image restoration Information Storage and Retrieval - methods Intelligence jump-preserving estimation Kernel local linear fit Mathematical analysis Mathematical models Models, Statistical noise nonparametric regression Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Preserves smoothing Smoothing methods Surface cleaning Surface fitting weighted residual mean square |
| title | Edge-preserving image denoising and estimation of discontinuous surfaces |
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