A weighted variational method for the removal of mixed noise
In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of im...
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| creator | Barcelos, C. A. Z. Barcelos, E. Z. |
| description | In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of images corrupted by non-Gaussian noise has not yet been thoroughly studied. The proposed model includes a balance between the data term and the regularization term in the energy functional, taking into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given observed noisy image. The obtained results have shown the effectiveness and robustness in restoring images with multiplicative noise or mixed Gaussian noise, while preserving edges and small structures belonging to the image. |
| doi_str_mv | 10.1109/ICSMC.2012.6377773 |
| format | Conference Proceeding |
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A. Z. ; Barcelos, E. Z.</creator><creatorcontrib>Barcelos, C. A. Z. ; Barcelos, E. Z.</creatorcontrib><description>In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of images corrupted by non-Gaussian noise has not yet been thoroughly studied. The proposed model includes a balance between the data term and the regularization term in the energy functional, taking into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given observed noisy image. 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A. Z.</creatorcontrib><creatorcontrib>Barcelos, E. Z.</creatorcontrib><title>A weighted variational method for the removal of mixed noise</title><title>2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)</title><addtitle>ICSMC</addtitle><description>In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of images corrupted by non-Gaussian noise has not yet been thoroughly studied. The proposed model includes a balance between the data term and the regularization term in the energy functional, taking into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given observed noisy image. The obtained results have shown the effectiveness and robustness in restoring images with multiplicative noise or mixed Gaussian noise, while preserving edges and small structures belonging to the image.</description><subject>Image denoising</subject><subject>Image edge detection</subject><subject>Image reconstruction</subject><subject>Impulsive noise</subject><subject>Mathematical model</subject><subject>Mixed noise</subject><subject>Mixture of Gaussians</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Noise reduction</subject><subject>Speckle</subject><subject>Variational pde model</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>9781467317139</isbn><isbn>1467317136</isbn><isbn>1467317144</isbn><isbn>9781467317122</isbn><isbn>9781467317146</isbn><isbn>1467317128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kEtLw0AUhccXmNb-Ad3MH0icO4_cDLgpwUeh4kIFd2WSuWNGmkaSUPXfG7CezYHzHc7iMHYJIgMQ9npVPj-WmRQgs1zhJHXEZqBzVICg9TFLpEFMITfmhC0sFv9M2VOWgMhlaqV8O2ezYfgQQgoNRcJulvyL4nszkud710c3xm7ntrylsek8D13Px4Z4T223n-Iu8DZ-T91dFwe6YGfBbQdaHHzOXu9uX8qHdP10vyqX6zQCmjGloCqota5UbhFrjzroipwvRFAGvasM-QlZL2SBEyEywRQAdbAVkqvUnF397caJbT772Lr-Z3N4Qf0Cc-ZNXg</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Barcelos, C. A. Z.</creator><creator>Barcelos, E. Z.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201210</creationdate><title>A weighted variational method for the removal of mixed noise</title><author>Barcelos, C. A. Z. ; Barcelos, E. Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-ef3b1c44b36977cd74f4bead80f357dab5ed3699d02874beee5f5811cf9b7eab3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Image denoising</topic><topic>Image edge detection</topic><topic>Image reconstruction</topic><topic>Impulsive noise</topic><topic>Mathematical model</topic><topic>Mixed noise</topic><topic>Mixture of Gaussians</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Noise reduction</topic><topic>Speckle</topic><topic>Variational pde model</topic><toplevel>online_resources</toplevel><creatorcontrib>Barcelos, C. A. Z.</creatorcontrib><creatorcontrib>Barcelos, E. Z.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Barcelos, C. A. Z.</au><au>Barcelos, E. Z.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A weighted variational method for the removal of mixed noise</atitle><btitle>2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)</btitle><stitle>ICSMC</stitle><date>2012-10</date><risdate>2012</risdate><spage>496</spage><epage>501</epage><pages>496-501</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>9781467317139</isbn><isbn>1467317136</isbn><eisbn>1467317144</eisbn><eisbn>9781467317122</eisbn><eisbn>9781467317146</eisbn><eisbn>1467317128</eisbn><abstract>In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of images corrupted by non-Gaussian noise has not yet been thoroughly studied. The proposed model includes a balance between the data term and the regularization term in the energy functional, taking into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given observed noisy image. The obtained results have shown the effectiveness and robustness in restoring images with multiplicative noise or mixed Gaussian noise, while preserving edges and small structures belonging to the image.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2012.6377773</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012, p.496-501 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_6377773 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Image denoising Image edge detection Image reconstruction Impulsive noise Mathematical model Mixed noise Mixture of Gaussians Noise Noise measurement Noise reduction Speckle Variational pde model |
| title | A weighted variational method for the removal of mixed noise |
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