{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:55:51Z","timestamp":1742975751054,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031189098"},{"type":"electronic","value":"9783031189104"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18910-4_19","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"227-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm"],"prefix":"10.1007","author":[{"given":"Chenxi","family":"Li","sequence":"first","affiliation":[]},{"given":"Yitong","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Fan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/RBME.2021.3055556","volume":"15","author":"PK Mishro","year":"2022","unstructured":"Mishro, P.K., Agrawal, S., Panda, R., et al.: A survey on state-of-the-art denoising techniques for brain magnetic resonance images. IEEE Rev. Biomed. Eng. 15, 184\u2013199 (2022)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Mallat, St.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397\u20133415 (1993)","DOI":"10.1109\/78.258082"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Bergeaud, F., Mallat, S.: Matching pursuit of images. In: Proceedings of the Proceedings. International Conference on Image Processing, Washington, DC, USA, pp. 53\u201356. IEEE (1995)","DOI":"10.1109\/ICIP.1995.529037"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Zhao, J., Xia, B.: An improved orthogonal matching pursuit based on randomly enhanced adaptive subspace pursuit. In: Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia, pp. 437\u2013441. IEEE (2017)","DOI":"10.1109\/APSIPA.2017.8282071"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Liu, W., Chen, X.: Research on identification algorithm based on optimal orthogonal matching pursuit. In: Proceedings of the 2021 4th International Conference on Electron Device and Mechanical Engineering (ICEDME), Guangzhou, China, pp. 185\u2013188. IEEE (2021)","DOI":"10.1109\/ICEDME52809.2021.00047"},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","volume":"3","author":"Z Zhang","year":"2015","unstructured":"Zhang, Z., Xu, Y., Yang, J., et al.: A survey of sparse representation: algorithms and applications. IEEE Access 3, 490\u2013530 (2015)","journal-title":"IEEE Access"},{"issue":"5","key":"19_CR7","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TIT.2014.2310482","volume":"60","author":"S Kwon","year":"2014","unstructured":"Kwon, S., Wang, J., Shim, B.: Multipath matching pursuit. IEEE Trans. Inf. Theory 60(5), 2986\u20133001 (2014)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"07","key":"19_CR8","first-page":"1308","volume":"43","author":"XU Xiao-dong","year":"2015","unstructured":"Xiao-dong, X.U., Ying-jie, L.E.I., Shao-hua, Y.U.E., Ying, H.E.: Research of PSO-based intuitionistic fuzzy kernel matching pursuit algorithm. Acta Electronica Sinica 43(07), 1308\u20131314 (2015)","journal-title":"Acta Electronica Sinica"},{"issue":"07","key":"19_CR9","first-page":"3086","volume":"61","author":"J Li","year":"2018","unstructured":"Li, J., Yan, H., Tang, J., Zhang, X., Li, G., Zhu, H.: Magnetotelluric noise suppression based on matching pursuit and genetic algorithm. Chin. J. Geophys. 61(07), 3086\u20133101 (2018)","journal-title":"Chin. J. Geophys."},{"issue":"3","key":"19_CR10","first-page":"295","volume":"39","author":"H Fan","year":"2005","unstructured":"Fan, H., Meng, Q.-F., Zhang, Y.: Matching pursuit via genetic algorithm based on hybrid coding. J. Xi\u2019an Jiaotong Univ. 39(3), 295\u2013299 (2005)","journal-title":"J. Xi\u2019an Jiaotong Univ."},{"issue":"4","key":"19_CR11","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution \u2013 a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"issue":"12","key":"19_CR12","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736\u20133745 (2006)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 60\u201365. IEEE (2005)","DOI":"10.1109\/CVPR.2005.38"},{"issue":"8","key":"19_CR14","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"19_CR15","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zuo, W., Chen, Y.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"108085","DOI":"10.1016\/j.patcog.2021.108085","volume":"119","author":"A Cl","year":"2021","unstructured":"Cl, A., Yh, B., Wei, H.C., et al.: Learning features from covariance matrix of Gabor wavelet for face recognition under adverse conditions. Pattern Recogn. 119, 108085\u2013108097 (2021)","journal-title":"Pattern Recogn."},{"issue":"7","key":"19_CR17","doi-asserted-by":"publisher","first-page":"4437","DOI":"10.1109\/TII.2020.3016317","volume":"17","author":"J Liu","year":"2020","unstructured":"Liu, J., Zhao, S., Xie, Y., et al.: Learning local Gabor pattern-based discriminative dictionary of froth images for flotation process working condition monitoring. IEEE Trans. Industr. Inf. 17(7), 4437\u20134448 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"10","key":"19_CR18","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1109\/34.541406","volume":"18","author":"TS Jone","year":"1996","unstructured":"Jone, T.S.: Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959\u2013971 (1996)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR19","doi-asserted-by":"publisher","first-page":"4242","DOI":"10.1109\/TCYB.2019.2909763","volume":"50","author":"J Liu","year":"2019","unstructured":"Liu, J., Zhou, J., et al.: Toward flotation process operation-state identification via statistical modeling of biologically inspired gabor filtering responses. IEEE Trans. Cybern. 50, 4242\u20134255 (2019)","journal-title":"IEEE Trans. Cybern."},{"issue":"6","key":"19_CR20","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1152\/jn.1987.58.6.1233","volume":"58","author":"JP Jones","year":"1987","unstructured":"Jones, J.P., Palmer, L.A.: An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J. Neurophysiol. 58(6), 1233\u20131258 (1987)","journal-title":"J. Neurophysiol."},{"issue":"13","key":"19_CR21","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1049\/el:20080522","volume":"44","author":"Q Huynh-Thu","year":"2008","unstructured":"Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image\/video quality assessment. Electron. Lett. 44(13), 800\u2013801 (2008)","journal-title":"Electron. Lett."},{"issue":"4","key":"19_CR22","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Qiang, G., Duan, C., Fang, X., et al.: A study on matching pursuit based on genetic algorithm. In: Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation (2011)","DOI":"10.1109\/ICMTMA.2011.860"},{"key":"19_CR24","unstructured":"Ventura, R., Vandergheynst, P., Pierre, V.: Matching pursuit through genetic algorithms. Technical report: 86783, Signal Processing Laboratories LTS2, Lausanne, Switzerland (2001)"},{"key":"19_CR25","unstructured":"Wang, X.-P., Cao, L.-M.: Genetic Algorithms: Theory, Applications, and Software Implementation. Xi\u2019an Jiaotong University Press, Xi\u2019an (2002)"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fbuil.2020.00102","volume":"6","author":"M Georgioudakis","year":"2020","unstructured":"Georgioudakis, M., Plevris, V.: A comparative study of differential evolution variants in constrained structural optimization. Front. Built Environ. 6, 1\u201314 (2020)","journal-title":"Front. Built Environ."},{"issue":"2","key":"19_CR27","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/S1051-2004(02)00028-3","volume":"13","author":"ARF Da Silva","year":"2003","unstructured":"Da Silva, A.R.F.: Atomic decomposition with evolutionary pursuit. Digit. Signal Process. 13(2), 317\u2013337 (2003)","journal-title":"Digit. Signal Process."},{"issue":"12","key":"19_CR28","doi-asserted-by":"publisher","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","volume":"53","author":"JA Tropp","year":"2007","unstructured":"Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655\u20134666 (2007)","journal-title":"IEEE Trans. Inf. Theory"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18910-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T16:35:17Z","timestamp":1728232517000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18910-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189098","9783031189104"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18910-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}