Quantum image edge extraction based on classical robinson operator
In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directio...
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| Veröffentlicht in: | Multimedia tools and applications 2022-09, Vol.81 (23), p.33459-33481 |
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| container_title | Multimedia tools and applications |
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| creator | Chakraborty, Sanjay Shaikh, Soharab Hossain Chakrabarti, Amlan Ghosh, Ranjan |
| description | In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directions and perform convolution operations with the quantum shifted image sets. In this paper, a quantum parallel computation is used for evaluating gradients of the image intensity of all pixels, and a threshold-based quantum black box is designed to classify the points as edge points. The computational complexity of the proposed scheme for an image of size 2
n
× 2
n
is O(
n
2
+ 2
q
+ 3
). However, we also carry out the design and simulation analysis of our proposed algorithm and finally compare our results with some state-of-art image edge extraction algorithms in terms of PSNR (peak signal to noise ratio), MSE (mean square error) and execution time. |
| doi_str_mv | 10.1007/s11042-022-12627-3 |
| format | Article |
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n
× 2
n
is O(
n
2
+ 2
q
+ 3
). However, we also carry out the design and simulation analysis of our proposed algorithm and finally compare our results with some state-of-art image edge extraction algorithms in terms of PSNR (peak signal to noise ratio), MSE (mean square error) and execution time.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-022-12627-3</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Breast cancer ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Entropy ; Image enhancement ; Methods ; Multimedia ; Multimedia Information Systems ; Parallel processing ; Quantum computing ; Signal to noise ratio ; Special Purpose and Application-Based Systems</subject><ispartof>Multimedia tools and applications, 2022-09, Vol.81 (23), p.33459-33481</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-cf24f109a41238dcd4e095c19166c8982b181cb6752e5428b927bcf4a120b19f3</citedby><cites>FETCH-LOGICAL-c249t-cf24f109a41238dcd4e095c19166c8982b181cb6752e5428b927bcf4a120b19f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-022-12627-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-022-12627-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Chakraborty, Sanjay</creatorcontrib><creatorcontrib>Shaikh, Soharab Hossain</creatorcontrib><creatorcontrib>Chakrabarti, Amlan</creatorcontrib><creatorcontrib>Ghosh, Ranjan</creatorcontrib><title>Quantum image edge extraction based on classical robinson operator</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directions and perform convolution operations with the quantum shifted image sets. In this paper, a quantum parallel computation is used for evaluating gradients of the image intensity of all pixels, and a threshold-based quantum black box is designed to classify the points as edge points. The computational complexity of the proposed scheme for an image of size 2
n
× 2
n
is O(
n
2
+ 2
q
+ 3
). 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Shaikh, Soharab Hossain ; Chakrabarti, Amlan ; Ghosh, Ranjan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c249t-cf24f109a41238dcd4e095c19166c8982b181cb6752e5428b927bcf4a120b19f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Breast cancer</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Data Structures and Information Theory</topic><topic>Entropy</topic><topic>Image enhancement</topic><topic>Methods</topic><topic>Multimedia</topic><topic>Multimedia Information Systems</topic><topic>Parallel processing</topic><topic>Quantum computing</topic><topic>Signal to noise ratio</topic><topic>Special Purpose and Application-Based Systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chakraborty, Sanjay</creatorcontrib><creatorcontrib>Shaikh, Soharab Hossain</creatorcontrib><creatorcontrib>Chakrabarti, Amlan</creatorcontrib><creatorcontrib>Ghosh, Ranjan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chakraborty, Sanjay</au><au>Shaikh, Soharab Hossain</au><au>Chakrabarti, Amlan</au><au>Ghosh, Ranjan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantum image edge extraction based on classical robinson operator</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2022-09-01</date><risdate>2022</risdate><volume>81</volume><issue>23</issue><spage>33459</spage><epage>33481</epage><pages>33459-33481</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>In this paper, a quantum image edge extraction technique is developed with the help of the classical Robinson operator. A novel enhanced quantum representation (NEQR) technique is used to represent the quantum image. A quantum methodology is proposed to implement the Robinson masks of eight directions and perform convolution operations with the quantum shifted image sets. In this paper, a quantum parallel computation is used for evaluating gradients of the image intensity of all pixels, and a threshold-based quantum black box is designed to classify the points as edge points. The computational complexity of the proposed scheme for an image of size 2
n
× 2
n
is O(
n
2
+ 2
q
+ 3
). However, we also carry out the design and simulation analysis of our proposed algorithm and finally compare our results with some state-of-art image edge extraction algorithms in terms of PSNR (peak signal to noise ratio), MSE (mean square error) and execution time.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-022-12627-3</doi><tpages>23</tpages></addata></record> |
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| subjects | Algorithms Breast cancer Computer Communication Networks Computer Science Data Structures and Information Theory Entropy Image enhancement Methods Multimedia Multimedia Information Systems Parallel processing Quantum computing Signal to noise ratio Special Purpose and Application-Based Systems |
| title | Quantum image edge extraction based on classical robinson operator |
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