Greyscale Image Interpolation Using Mathematical Morphology
When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”. Earlier we propose...
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| creator | Ledda, Alessandro Luong, Hiêp Q. Philips, Wilfried De Witte, Valérie Kerre, Etienne E. |
| description | When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”.
Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.
In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method. |
| doi_str_mv | 10.1007/11864349_8 |
| format | Book Chapter |
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Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.
In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540446303</identifier><identifier>ISBN: 9783540446309</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540446323</identifier><identifier>EISBN: 354044632X</identifier><identifier>DOI: 10.1007/11864349_8</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Corner Detection ; Current Pixel ; Exact sciences and technology ; Foreground Pixel ; Image Interpolation ; Mathematical Morphology ; Pattern recognition. Digital image processing. Computational geometry</subject><ispartof>Advanced Concepts for Intelligent Vision Systems, 2006, p.78-90</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11864349_8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11864349_8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19162452$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Blanc-Talon, Jacques</contributor><contributor>Popescu, Dan</contributor><contributor>Philips, Wilfried</contributor><contributor>Scheunders, Paul</contributor><creatorcontrib>Ledda, Alessandro</creatorcontrib><creatorcontrib>Luong, Hiêp Q.</creatorcontrib><creatorcontrib>Philips, Wilfried</creatorcontrib><creatorcontrib>De Witte, Valérie</creatorcontrib><creatorcontrib>Kerre, Etienne E.</creatorcontrib><title>Greyscale Image Interpolation Using Mathematical Morphology</title><title>Advanced Concepts for Intelligent Vision Systems</title><description>When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”.
Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.
In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Corner Detection</subject><subject>Current Pixel</subject><subject>Exact sciences and technology</subject><subject>Foreground Pixel</subject><subject>Image Interpolation</subject><subject>Mathematical Morphology</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540446303</isbn><isbn>9783540446309</isbn><isbn>9783540446323</isbn><isbn>354044632X</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><recordid>eNpFkE1PwzAMhsOXxBi78At6QeJSiOM0H-KEJhiTNnFh5yjt0q7QNVXSy_49mQbCB1vy88iyXkLugD4CpfIJQAmOXBt1RmZaKiw45Vwgw3MyAQGQY6IX5OYPULwkE4qU5VpyvCazGL9oKoRCgpiQ50Vwh1jZzmXLvW1S70cXBt_ZsfV9tolt32RrO-7cPm2Sl619GHa-883hllzVtotu9junZPP2-jl_z1cfi-X8ZZUPrOBjbkuGSJ1QTJeVE0LTSmngSm6tLSRuZcURJaNSJw8r5LS2wECBoKVQusApuT_dHezx0zrYvmqjGUK7t-FgQINgvGDJezh5MaG-ccGU3n9HA9QcwzP_4eEPZlxZwA</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Ledda, Alessandro</creator><creator>Luong, Hiêp Q.</creator><creator>Philips, Wilfried</creator><creator>De Witte, Valérie</creator><creator>Kerre, Etienne E.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Greyscale Image Interpolation Using Mathematical Morphology</title><author>Ledda, Alessandro ; Luong, Hiêp Q. ; Philips, Wilfried ; De Witte, Valérie ; Kerre, Etienne E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p254t-ab2330e6829bce6690c891487daa573d7c433720792333c340fa1218160b68953</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Corner Detection</topic><topic>Current Pixel</topic><topic>Exact sciences and technology</topic><topic>Foreground Pixel</topic><topic>Image Interpolation</topic><topic>Mathematical Morphology</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ledda, Alessandro</creatorcontrib><creatorcontrib>Luong, Hiêp Q.</creatorcontrib><creatorcontrib>Philips, Wilfried</creatorcontrib><creatorcontrib>De Witte, Valérie</creatorcontrib><creatorcontrib>Kerre, Etienne E.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ledda, Alessandro</au><au>Luong, Hiêp Q.</au><au>Philips, Wilfried</au><au>De Witte, Valérie</au><au>Kerre, Etienne E.</au><au>Blanc-Talon, Jacques</au><au>Popescu, Dan</au><au>Philips, Wilfried</au><au>Scheunders, Paul</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Greyscale Image Interpolation Using Mathematical Morphology</atitle><btitle>Advanced Concepts for Intelligent Vision Systems</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>78</spage><epage>90</epage><pages>78-90</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540446303</isbn><isbn>9783540446309</isbn><eisbn>9783540446323</eisbn><eisbn>354044632X</eisbn><abstract>When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”.
Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.
In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11864349_8</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 0302-9743 1611-3349 |
| language | eng |
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| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Corner Detection Current Pixel Exact sciences and technology Foreground Pixel Image Interpolation Mathematical Morphology Pattern recognition. Digital image processing. Computational geometry |
| title | Greyscale Image Interpolation Using Mathematical Morphology |
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