Image denoising using common vector approach
Common vector approach (CVA) is an increasingly popular classification method in recognition problems where probability of having the dimensionality of the problem higher than the number of data items is not zero. In CVA, common component of the members of classes is separated from the discriminatin...
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| Veröffentlicht in: | IET image processing 2015-08, Vol.9 (8), p.709-715 |
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| container_title | IET image processing |
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| creator | Özkan, Kemal Seke, Erol |
| description | Common vector approach (CVA) is an increasingly popular classification method in recognition problems where probability of having the dimensionality of the problem higher than the number of data items is not zero. In CVA, common component of the members of classes is separated from the discriminating difference parts and used to determine whether a given vector (a block of data) belongs to the class in question, or to find out the class it belongs to. In this study, overlapping image blocks near the current pixel to be denoised are used as input data and a class is constructed per pixel position. Denoised image block is then constructed with the sum of common vector of the class and difference vector of the centre block denoised by linear minimum mean square error estimation technique. Since the classes are formed using similar blocks, the edges are preserved while denoising the image. |
| doi_str_mv | 10.1049/iet-ipr.2014.0979 |
| format | Article |
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Since the classes are formed using similar blocks, the edges are preserved while denoising the image.</description><subject>Blocking</subject><subject>Classification</subject><subject>common vector approach</subject><subject>Construction</subject><subject>CVA</subject><subject>estimation theory</subject><subject>image classification</subject><subject>image classification method</subject><subject>image denoising</subject><subject>image recognition</subject><subject>linear minimum mean square error estimation technique</subject><subject>Mathematical analysis</subject><subject>mean square error methods</subject><subject>Noise reduction</subject><subject>Pixels</subject><subject>probability</subject><subject>Review Articles</subject><subject>vectors</subject><subject>Vectors (mathematics)</subject><issn>1751-9659</issn><issn>1751-9667</issn><issn>1751-9667</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkE1Lw0AQhoMoWKs_wFuOCqbObvaj601Lq4GCIvW8bDabmpJk426j9N-bGBEPol5m5vC888ITBKcIJgiIuCzMNioaN8GAyAQEF3vBCHGKIsEY3_-6qTgMjrzfAFABUzoKLpJKrU2YmdoWvqjXYfsxta0qW4evRm-tC1XTOKv083FwkKvSm5PPPQ6eFvPV7C5a3t8ms-tlpCkCiARJATKtjaZprDImFM4RjTFJiRHc8BTHqZnmQECIFFGMBIMMgKUGECdIxePgbPjb1b60xm9lVXhtylLVxrZeIi5iTDnD_L8oIuJ_KCaI4Q5FA6qd9d6ZXDauqJTbSQSy9y0737LzLXvfsvfdZa6GzFtRmt3fAZk8POKbBWAmoAtHQ7jHNrZ1def317LzH_hkvuq_futosjx-B8NIoSE</recordid><startdate>201508</startdate><enddate>201508</enddate><creator>Özkan, Kemal</creator><creator>Seke, Erol</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201508</creationdate><title>Image denoising using common vector approach</title><author>Özkan, Kemal ; Seke, Erol</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5100-94b00dccec5b3ad69a2f15324b4e97e7b23be8f04099b1521960d006be01741a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Blocking</topic><topic>Classification</topic><topic>common vector approach</topic><topic>Construction</topic><topic>CVA</topic><topic>estimation theory</topic><topic>image classification</topic><topic>image classification method</topic><topic>image denoising</topic><topic>image recognition</topic><topic>linear minimum mean square error estimation technique</topic><topic>Mathematical analysis</topic><topic>mean square error methods</topic><topic>Noise reduction</topic><topic>Pixels</topic><topic>probability</topic><topic>Review Articles</topic><topic>vectors</topic><topic>Vectors (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Özkan, Kemal</creatorcontrib><creatorcontrib>Seke, Erol</creatorcontrib><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><jtitle>IET image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Özkan, Kemal</au><au>Seke, Erol</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image denoising using common vector approach</atitle><jtitle>IET image processing</jtitle><date>2015-08</date><risdate>2015</risdate><volume>9</volume><issue>8</issue><spage>709</spage><epage>715</epage><pages>709-715</pages><issn>1751-9659</issn><issn>1751-9667</issn><eissn>1751-9667</eissn><abstract>Common vector approach (CVA) is an increasingly popular classification method in recognition problems where probability of having the dimensionality of the problem higher than the number of data items is not zero. 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| subjects | Blocking Classification common vector approach Construction CVA estimation theory image classification image classification method image denoising image recognition linear minimum mean square error estimation technique Mathematical analysis mean square error methods Noise reduction Pixels probability Review Articles vectors Vectors (mathematics) |
| title | Image denoising using common vector approach |
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