Real-time endoscopy haze removal: a synthetical method
In endoscopic surgeries, the smoke and haze generated by temperature difference and electrosurgical knife usage degrades the surgical view. While many researches on natural single image haze removal being proposed, few works are done for video haze removal of endoscopy view. In this paper, we propos...
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| Veröffentlicht in: | Multimedia tools and applications 2024-03, Vol.83 (11), p.31195-31209 |
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| creator | Zhuo, Xu Yang, Chenbin Gao, Yuming Wang, Zhihua Chen, Yang Wu, Ke |
| description | In endoscopic surgeries, the smoke and haze generated by temperature difference and electrosurgical knife usage degrades the surgical view. While many researches on natural single image haze removal being proposed, few works are done for video haze removal of endoscopy view. In this paper, we proposed a synthetical method for endoscopy haze removal combining a
Refined Dark Channel Prior
(RDCP),
Spatial-Temporal Markov Random Field
(STMRF),
Color Attenuation Prior
(CAP) and parameter self-adaption. Qualitative and quantitative experiment results outperformed some traditional and deep learning based methods. Our proposed method is an effective and practical one. |
| doi_str_mv | 10.1007/s11042-023-16375-w |
| format | Article |
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Refined Dark Channel Prior
(RDCP),
Spatial-Temporal Markov Random Field
(STMRF),
Color Attenuation Prior
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Refined Dark Channel Prior
(RDCP),
Spatial-Temporal Markov Random Field
(STMRF),
Color Attenuation Prior
(CAP) and parameter self-adaption. Qualitative and quantitative experiment results outperformed some traditional and deep learning based methods. Our proposed method is an effective and practical one.</description><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Data Structures and Information Theory</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Endoscopy</subject><subject>Fields (mathematics)</subject><subject>Haze</subject><subject>Light</subject><subject>Medical research</subject><subject>Methods</subject><subject>Multimedia</subject><subject>Multimedia Information Systems</subject><subject>Neural networks</subject><subject>Special Purpose and Application-Based Systems</subject><issn>1573-7721</issn><issn>1380-7501</issn><issn>1573-7721</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kFFLwzAUhYMoOKd_wKeCz9Hc26ZZfZOhUxgIos8hS29tR9vMpHPMX2-0gj75dA7cc86Fj7FzEJcghLoKACJDLjDlkKdK8t0Bm4BUKVcK4fCPP2YnIayFgFxiNmH5E5mWD01HCfWlC9Zt9kltPijx1Ll3014nJgn7fqhpaKxpk46G2pWn7KgybaCzH52yl7vb5_k9Xz4uHuY3S25RiYHbjJScrSqlKMsRISe0hazszAABERYlzkSlkHKSFimFeKxgtYpSFpKKdMouxt2Nd29bCoNeu63v40uNhUQoMshUTOGYst6F4KnSG990xu81CP3FR498dOSjv_noXSylYynEcP9K_nf6n9YncTBoZg</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Zhuo, Xu</creator><creator>Yang, Chenbin</creator><creator>Gao, Yuming</creator><creator>Wang, Zhihua</creator><creator>Chen, Yang</creator><creator>Wu, Ke</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-5660-6349</orcidid></search><sort><creationdate>20240301</creationdate><title>Real-time endoscopy haze removal: a synthetical method</title><author>Zhuo, Xu ; Yang, Chenbin ; Gao, Yuming ; Wang, Zhihua ; Chen, Yang ; Wu, Ke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-c4e758bf77e462216e2c95fc8a1e1ee29d280f72e6e5c2e3195ff1bb95fd95e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Data Structures and Information Theory</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Endoscopy</topic><topic>Fields (mathematics)</topic><topic>Haze</topic><topic>Light</topic><topic>Medical research</topic><topic>Methods</topic><topic>Multimedia</topic><topic>Multimedia Information Systems</topic><topic>Neural networks</topic><topic>Special Purpose and Application-Based Systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhuo, Xu</creatorcontrib><creatorcontrib>Yang, Chenbin</creatorcontrib><creatorcontrib>Gao, Yuming</creatorcontrib><creatorcontrib>Wang, Zhihua</creatorcontrib><creatorcontrib>Chen, Yang</creatorcontrib><creatorcontrib>Wu, Ke</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhuo, Xu</au><au>Yang, Chenbin</au><au>Gao, Yuming</au><au>Wang, Zhihua</au><au>Chen, Yang</au><au>Wu, Ke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time endoscopy haze removal: a synthetical method</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2024-03-01</date><risdate>2024</risdate><volume>83</volume><issue>11</issue><spage>31195</spage><epage>31209</epage><pages>31195-31209</pages><issn>1573-7721</issn><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>In endoscopic surgeries, the smoke and haze generated by temperature difference and electrosurgical knife usage degrades the surgical view. While many researches on natural single image haze removal being proposed, few works are done for video haze removal of endoscopy view. In this paper, we proposed a synthetical method for endoscopy haze removal combining a
Refined Dark Channel Prior
(RDCP),
Spatial-Temporal Markov Random Field
(STMRF),
Color Attenuation Prior
(CAP) and parameter self-adaption. Qualitative and quantitative experiment results outperformed some traditional and deep learning based methods. Our proposed method is an effective and practical one.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-023-16375-w</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-5660-6349</orcidid></addata></record> |
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| subjects | Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Deep learning Endoscopy Fields (mathematics) Haze Light Medical research Methods Multimedia Multimedia Information Systems Neural networks Special Purpose and Application-Based Systems |
| title | Real-time endoscopy haze removal: a synthetical method |
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