Determining the parameters in regularized super-resolution reconstruction
We derive a novel method to determine the parameters for regularized super-resolution problems. The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents m...
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| creator | Zibetti, M.V.W. Mayer, J. Bazan, F.S.V. |
| description | We derive a novel method to determine the parameters for regularized super-resolution problems. The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by the best methods such as the Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method. |
| doi_str_mv | 10.1109/ICASSP.2008.4517744 |
| format | Conference Proceeding |
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The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by the best methods such as the Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method.</description><subject>Bayesian estimation</subject><subject>Bayesian methods</subject><subject>Computational efficiency</subject><subject>Cost function</subject><subject>Interpolation</subject><subject>Iterative methods</subject><subject>JMAP</subject><subject>Motion estimation</subject><subject>Parameter estimation</subject><subject>Pixel</subject><subject>regularization</subject><subject>Stability</subject><subject>Strontium</subject><subject>Super-resolution</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424414833</isbn><isbn>1424414830</isbn><isbn>1424414849</isbn><isbn>9781424414840</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UNtKxDAUjDdwXfsF-9IfaM1pTprmUdbbwoLCKvi2pPF0jfRG0j7o19viOi_DzMAwDGMr4CkA1zeb9e1u95JmnBcpSlAK8YRdAWaIgAXqU7bIhNIJaP5-xiKtiv9MiHO2AJnxJAfUlywK4YtPQCmklgu2uaOBfONa1x7i4ZPi3njTzF6IXRt7Ooy18e6HPuIw9uQTT6Grx8F1c2i7Ngx-tLO8ZheVqQNFR16yt4f71_VTsn1-nPZvEwdKDgkqA5xQAxlheVYVJRZGVSSmRbkxJSiubVWaLEcrOGgqlS2RLDdQaiisWLLVX68jon3vXWP89_74ivgFGdZUDA</recordid><startdate>200803</startdate><enddate>200803</enddate><creator>Zibetti, M.V.W.</creator><creator>Mayer, J.</creator><creator>Bazan, F.S.V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200803</creationdate><title>Determining the parameters in regularized super-resolution reconstruction</title><author>Zibetti, M.V.W. ; Mayer, J. ; Bazan, F.S.V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-47a10e491ea3c02f8b48a7fe34536aab1709cfba264c3019eb7cb4ec0a1b918c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Bayesian estimation</topic><topic>Bayesian methods</topic><topic>Computational efficiency</topic><topic>Cost function</topic><topic>Interpolation</topic><topic>Iterative methods</topic><topic>JMAP</topic><topic>Motion estimation</topic><topic>Parameter estimation</topic><topic>Pixel</topic><topic>regularization</topic><topic>Stability</topic><topic>Strontium</topic><topic>Super-resolution</topic><toplevel>online_resources</toplevel><creatorcontrib>Zibetti, M.V.W.</creatorcontrib><creatorcontrib>Mayer, J.</creatorcontrib><creatorcontrib>Bazan, F.S.V.</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>Zibetti, M.V.W.</au><au>Mayer, J.</au><au>Bazan, F.S.V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Determining the parameters in regularized super-resolution reconstruction</atitle><btitle>2008 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2008-03</date><risdate>2008</risdate><spage>853</spage><epage>856</epage><pages>853-856</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424414833</isbn><isbn>1424414830</isbn><eisbn>1424414849</eisbn><eisbn>9781424414840</eisbn><abstract>We derive a novel method to determine the parameters for regularized super-resolution problems. The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by the best methods such as the Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2008.4517744</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.853-856 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_4517744 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian estimation Bayesian methods Computational efficiency Cost function Interpolation Iterative methods JMAP Motion estimation Parameter estimation Pixel regularization Stability Strontium Super-resolution |
| title | Determining the parameters in regularized super-resolution reconstruction |
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