ICRICS: iterative compensation recovery for image compressive sensing
Closed-loop architecture is widely utilized in automatic control systems and attains distinguished dynamic and static performance. However, classical compressive sensing systems employ an open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compen...
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Veröffentlicht in: | Signal, image and video processing image and video processing, 2023-09, Vol.17 (6), p.2953-2969 |
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creator | Li, Honggui Trocan, Maria Sawan, Mohamad Galayko, Dimitri |
description | Closed-loop architecture is widely utilized in automatic control systems and attains distinguished dynamic and static performance. However, classical compressive sensing systems employ an open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing is proposed by introducing a closed-loop framework into traditional compressive sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding a negative feedback structure. Theoretical analysis of the negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competing approaches in reconstruction performance. The maximum increment of the average peak signal-to-noise ratio is 4.36 dB, and the maximum increment of the average structural similarity is 0.034 based on one dataset. The proposed method based on a negative feedback mechanism can efficiently correct the recovery error in the existing image compressive sensing systems. |
doi_str_mv | 10.1007/s11760-023-02516-z |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c397t-7e2e872f5fcf9a869642f66f5a1d9ac95fd3b4b17bae8aaceb205ea7a877cca93</citedby><cites>FETCH-LOGICAL-c397t-7e2e872f5fcf9a869642f66f5a1d9ac95fd3b4b17bae8aaceb205ea7a877cca93</cites><orcidid>0000-0002-7056-7489</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11760-023-02516-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11760-023-02516-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27915,27916,41479,42548,51310</link.rule.ids><backlink>$$Uhttps://hal.science/hal-04031119$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Honggui</creatorcontrib><creatorcontrib>Trocan, Maria</creatorcontrib><creatorcontrib>Sawan, Mohamad</creatorcontrib><creatorcontrib>Galayko, Dimitri</creatorcontrib><title>ICRICS: iterative compensation recovery for image compressive sensing</title><title>Signal, image and video processing</title><addtitle>SIViP</addtitle><description>Closed-loop architecture is widely utilized in automatic control systems and attains distinguished dynamic and static performance. However, classical compressive sensing systems employ an open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing is proposed by introducing a closed-loop framework into traditional compressive sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding a negative feedback structure. Theoretical analysis of the negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competing approaches in reconstruction performance. The maximum increment of the average peak signal-to-noise ratio is 4.36 dB, and the maximum increment of the average structural similarity is 0.034 based on one dataset. The proposed method based on a negative feedback mechanism can efficiently correct the recovery error in the existing image compressive sensing systems.</description><subject>Automatic control systems</subject><subject>Closed loops</subject><subject>Compensation</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Datasets</subject><subject>Engineering Sciences</subject><subject>Error correction</subject><subject>Image Processing and Computer Vision</subject><subject>Iterative methods</subject><subject>Multimedia Information Systems</subject><subject>Negative feedback</subject><subject>Original Paper</subject><subject>Pattern Recognition and Graphics</subject><subject>Reconstruction</subject><subject>Recovery</subject><subject>Signal to noise ratio</subject><subject>Signal,Image and Speech Processing</subject><subject>System effectiveness</subject><subject>Vision</subject><issn>1863-1703</issn><issn>1863-1711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWLR_wNWAKxejuclMHu7KUG2hIPhYh0ya1CntpCbTQvvrTR2pOwMhN_d-53A5CN0AvgeM-UME4AznmNB0S2D54QwNQDCaAwc4P9WYXqJhjEucDiVcMDFA42n1Oq3eHrOms0F3zc5mxq83to3p49ssWON3Nuwz50PWrPWinwcb45GNCWzaxTW6cHoV7fD3vUIfT-P3apLPXp6n1WiWGyp5l3NLrODElc44qQWTrCCOMVdqmEttZOnmtC5q4LW2Qmtja4JLq7kWnBujJb1Cd73vp16pTUj7hL3yulGT0Uwde7jAFADkDhJ727Ob4L-2NnZq6behTespIggTuKCAE0V6ygQfY7DuZAtYHdNVfboqpat-0lWHJKK9KCa4XdjwZ_2P6htARX1G</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Li, Honggui</creator><creator>Trocan, Maria</creator><creator>Sawan, Mohamad</creator><creator>Galayko, Dimitri</creator><general>Springer London</general><general>Springer Nature B.V</general><general>Springer Verlag</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-7056-7489</orcidid></search><sort><creationdate>20230901</creationdate><title>ICRICS: iterative compensation recovery for image compressive sensing</title><author>Li, Honggui ; Trocan, Maria ; Sawan, Mohamad ; Galayko, Dimitri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-7e2e872f5fcf9a869642f66f5a1d9ac95fd3b4b17bae8aaceb205ea7a877cca93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Automatic control systems</topic><topic>Closed loops</topic><topic>Compensation</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Datasets</topic><topic>Engineering Sciences</topic><topic>Error correction</topic><topic>Image Processing and Computer Vision</topic><topic>Iterative methods</topic><topic>Multimedia Information Systems</topic><topic>Negative feedback</topic><topic>Original Paper</topic><topic>Pattern Recognition and Graphics</topic><topic>Reconstruction</topic><topic>Recovery</topic><topic>Signal to noise ratio</topic><topic>Signal,Image and Speech Processing</topic><topic>System effectiveness</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Honggui</creatorcontrib><creatorcontrib>Trocan, Maria</creatorcontrib><creatorcontrib>Sawan, Mohamad</creatorcontrib><creatorcontrib>Galayko, Dimitri</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Signal, image and video processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Honggui</au><au>Trocan, Maria</au><au>Sawan, Mohamad</au><au>Galayko, Dimitri</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ICRICS: iterative compensation recovery for image compressive sensing</atitle><jtitle>Signal, image and video processing</jtitle><stitle>SIViP</stitle><date>2023-09-01</date><risdate>2023</risdate><volume>17</volume><issue>6</issue><spage>2953</spage><epage>2969</epage><pages>2953-2969</pages><issn>1863-1703</issn><eissn>1863-1711</eissn><abstract>Closed-loop architecture is widely utilized in automatic control systems and attains distinguished dynamic and static performance. However, classical compressive sensing systems employ an open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing is proposed by introducing a closed-loop framework into traditional compressive sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding a negative feedback structure. Theoretical analysis of the negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competing approaches in reconstruction performance. The maximum increment of the average peak signal-to-noise ratio is 4.36 dB, and the maximum increment of the average structural similarity is 0.034 based on one dataset. The proposed method based on a negative feedback mechanism can efficiently correct the recovery error in the existing image compressive sensing systems.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s11760-023-02516-z</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-7056-7489</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Automatic control systems Closed loops Compensation Computer Imaging Computer Science Datasets Engineering Sciences Error correction Image Processing and Computer Vision Iterative methods Multimedia Information Systems Negative feedback Original Paper Pattern Recognition and Graphics Reconstruction Recovery Signal to noise ratio Signal,Image and Speech Processing System effectiveness Vision |
title | ICRICS: iterative compensation recovery for image compressive sensing |
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