Digital Image Restoration Using Posterior Distribution and Updating Pixel by Self Threshold
Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm has been applied to simulate digital image restoration using posterior distribution and pixels update with self threshold method. This method was implemented to Lena image degraded with 0.15 th order salt and pepper noise by the variati...
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| creator | Adil, K. Mengko, T.L.R. Suksmono, A.B. Danudirdjo, D. |
| description | Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm has been applied to simulate digital image restoration using posterior distribution and pixels update with self threshold method. This method was implemented to Lena image degraded with 0.15 th order salt and pepper noise by the variation of temperature at 1.5, 2.5 and 4.5. The result of digital image restoration in gray scale at temperature 1.5 is good image restored with Delta SNR 13.072 dB. In this simulation, the number of Markov chains (1000 chains) and iteration (800 iteration) are fixed parameter |
| doi_str_mv | 10.1109/SICE.2006.314638 |
| format | Conference Proceeding |
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This method was implemented to Lena image degraded with 0.15 th order salt and pepper noise by the variation of temperature at 1.5, 2.5 and 4.5. The result of digital image restoration in gray scale at temperature 1.5 is good image restored with Delta SNR 13.072 dB. 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In this simulation, the number of Markov chains (1000 chains) and iteration (800 iteration) are fixed parameter</description><subject>Degradation</subject><subject>Digital images</subject><subject>Image restoration</subject><subject>Low pass filters</subject><subject>MHMCMC algorithm</subject><subject>Monte Carlo methods</subject><subject>Nonlinear filters</subject><subject>Pixel</subject><subject>Posteriori Distribution</subject><subject>Random variables</subject><subject>Self Threshold</subject><subject>Stochastic resonance</subject><subject>Temperature</subject><isbn>9788995003848</isbn><isbn>8995003847</isbn><isbn>9788995003855</isbn><isbn>8995003855</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVjM1KAzEYRSMiKHX2gpu8QOuX_2QpbdVCQbHTlYuSmfkyjUw7JRnBvr31Z-PdXC7ncAm5YTBhDNzdajGdTziAnggmtbBnpHDGWucUgLBKnf_b0l6SIud3OEU4rbi8Im-z2MbBd3Sx8y3SV8xDn_wQ-z1d57hv6UufB0yxT3QW85Bi9fED_b6h60NzMr-d-IkdrY50hV2g5TZh3vZdc00ugu8yFn89IuXDvJw-jZfPj4vp_XIcHQxj402omAXnmPehQsulq2up6lpDJUQIHHXtjQrArA0NrwEl15zbYIzRuhIjcvt7GxFxc0hx59NxIxlY5ZT4AvBWVOQ</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Adil, K.</creator><creator>Mengko, T.L.R.</creator><creator>Suksmono, A.B.</creator><creator>Danudirdjo, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200610</creationdate><title>Digital Image Restoration Using Posterior Distribution and Updating Pixel by Self Threshold</title><author>Adil, K. ; Mengko, T.L.R. ; Suksmono, A.B. ; Danudirdjo, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-7a7fb180991aafbe8249cc45cc60b33ff2e6ca75f0188fd2c0e426228f77766b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Degradation</topic><topic>Digital images</topic><topic>Image restoration</topic><topic>Low pass filters</topic><topic>MHMCMC algorithm</topic><topic>Monte Carlo methods</topic><topic>Nonlinear filters</topic><topic>Pixel</topic><topic>Posteriori Distribution</topic><topic>Random variables</topic><topic>Self Threshold</topic><topic>Stochastic resonance</topic><topic>Temperature</topic><toplevel>online_resources</toplevel><creatorcontrib>Adil, K.</creatorcontrib><creatorcontrib>Mengko, T.L.R.</creatorcontrib><creatorcontrib>Suksmono, A.B.</creatorcontrib><creatorcontrib>Danudirdjo, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Adil, K.</au><au>Mengko, T.L.R.</au><au>Suksmono, A.B.</au><au>Danudirdjo, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Digital Image Restoration Using Posterior Distribution and Updating Pixel by Self Threshold</atitle><btitle>2006 SICE-ICASE International Joint Conference</btitle><stitle>SICE</stitle><date>2006-10</date><risdate>2006</risdate><spage>5707</spage><epage>5710</epage><pages>5707-5710</pages><isbn>9788995003848</isbn><isbn>8995003847</isbn><eisbn>9788995003855</eisbn><eisbn>8995003855</eisbn><abstract>Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm has been applied to simulate digital image restoration using posterior distribution and pixels update with self threshold method. This method was implemented to Lena image degraded with 0.15 th order salt and pepper noise by the variation of temperature at 1.5, 2.5 and 4.5. The result of digital image restoration in gray scale at temperature 1.5 is good image restored with Delta SNR 13.072 dB. In this simulation, the number of Markov chains (1000 chains) and iteration (800 iteration) are fixed parameter</abstract><pub>IEEE</pub><doi>10.1109/SICE.2006.314638</doi><tpages>4</tpages></addata></record> |
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| subjects | Degradation Digital images Image restoration Low pass filters MHMCMC algorithm Monte Carlo methods Nonlinear filters Pixel Posteriori Distribution Random variables Self Threshold Stochastic resonance Temperature |
| title | Digital Image Restoration Using Posterior Distribution and Updating Pixel by Self Threshold |
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