Shift invariant restoration-an overcomplete maxent MAP framework
Translation-invariant denoising was introduced by Coifman and Donoho (1995) to overcome Gibbs-type phenomena produced by transform-domain shrinkage estimators in the vicinity of signal discontinuities. Shrinkage estimators are in general not shift-invariant. Shift-invariant denoising consists of a s...
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| creator | Ishwar, P. Moulin, P. |
| description | Translation-invariant denoising was introduced by Coifman and Donoho (1995) to overcome Gibbs-type phenomena produced by transform-domain shrinkage estimators in the vicinity of signal discontinuities. Shrinkage estimators are in general not shift-invariant. Shift-invariant denoising consists of a simple averaging of the shrinkage estimates over a family of cyclic spatial-shifts of the image. Shift-invariant denoising is denoising in an overcomplete basis, and work in this area has been devoted towards finding a best basis in the overcomplete family. This paper presents a maximum a posteriori (MAP) framework for shift-invariant restoration of images using the maximum-entropy prior consistent with moment constraints on the transform coefficients in different subbands. The simple averaging of estimates in the classical shift-invariant denoising can then be shown to be a certain limiting case within this framework. |
| doi_str_mv | 10.1109/ICIP.2000.899347 |
| format | Conference Proceeding |
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The simple averaging of estimates in the classical shift-invariant denoising can then be shown to be a certain limiting case within this framework.</description><subject>Additive white noise</subject><subject>AWGN</subject><subject>Bayesian methods</subject><subject>Gaussian noise</subject><subject>Image restoration</subject><subject>Kernel</subject><subject>Noise reduction</subject><subject>Pixel</subject><subject>Vectors</subject><subject>Wavelet coefficients</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>0780362977</isbn><isbn>9780780362970</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0tLw0AUhQcfYKzdi6v8gYl3Hpnc2VmCj0DFgt2XSXKDo01SJkPVf2-gns3ZfOeDw9itgEwIsPdVWW0yCQAZWqt0ccYSqVBwzLU9Z9dQICgjbVFcsETkUnKNCFdsOU2f8wh0rguUCXt4__BdTP1wdMG7IaaBpjgGF_04cDek45FCM_aHPUVKe_dDM_K62qRdcD19j-Hrhl12bj_R8r8XbPv0uC1f-PrtuSpXa-4F6MhzBYRt7RojdQO6buvauNqqxloEoTqDQmOOYFyHQuh2jkMitEZ2VIBasLuT1hPR7hB878Lv7nRd_QGLqkuA</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Ishwar, P.</creator><creator>Moulin, P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2000</creationdate><title>Shift invariant restoration-an overcomplete maxent MAP framework</title><author>Ishwar, P. ; Moulin, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-530e8dbac624c04bdbb6ab93c998013f681485806af8114dddda8ee8962fe703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Additive white noise</topic><topic>AWGN</topic><topic>Bayesian methods</topic><topic>Gaussian noise</topic><topic>Image restoration</topic><topic>Kernel</topic><topic>Noise reduction</topic><topic>Pixel</topic><topic>Vectors</topic><topic>Wavelet coefficients</topic><toplevel>online_resources</toplevel><creatorcontrib>Ishwar, P.</creatorcontrib><creatorcontrib>Moulin, P.</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>Ishwar, P.</au><au>Moulin, P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Shift invariant restoration-an overcomplete maxent MAP framework</atitle><btitle>Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)</btitle><stitle>ICIP</stitle><date>2000</date><risdate>2000</risdate><volume>3</volume><spage>270</spage><epage>272 vol.3</epage><pages>270-272 vol.3</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>0780362977</isbn><isbn>9780780362970</isbn><abstract>Translation-invariant denoising was introduced by Coifman and Donoho (1995) to overcome Gibbs-type phenomena produced by transform-domain shrinkage estimators in the vicinity of signal discontinuities. Shrinkage estimators are in general not shift-invariant. Shift-invariant denoising consists of a simple averaging of the shrinkage estimates over a family of cyclic spatial-shifts of the image. Shift-invariant denoising is denoising in an overcomplete basis, and work in this area has been devoted towards finding a best basis in the overcomplete family. This paper presents a maximum a posteriori (MAP) framework for shift-invariant restoration of images using the maximum-entropy prior consistent with moment constraints on the transform coefficients in different subbands. The simple averaging of estimates in the classical shift-invariant denoising can then be shown to be a certain limiting case within this framework.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2000.899347</doi></addata></record> |
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| ispartof | Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000, Vol.3, p.270-272 vol.3 |
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| language | eng |
| recordid | cdi_ieee_primary_899347 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Additive white noise AWGN Bayesian methods Gaussian noise Image restoration Kernel Noise reduction Pixel Vectors Wavelet coefficients |
| title | Shift invariant restoration-an overcomplete maxent MAP framework |
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