{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:16:05Z","timestamp":1742919365335,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031476648"},{"type":"electronic","value":"9783031476655"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47665-5_31","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:01:39Z","timestamp":1699128099000},"page":"383-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Composite Restoration of Infrared Image Based on Adaptive Threshold Multi-parameter Wavelet"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0772-2887","authenticated-orcid":false,"given":"Shuai","family":"Liu","sequence":"first","affiliation":[]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhengxiang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Zhanshan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Johnson, J.E., et al.: Comparison of long-wave infrared imaging and visible\/near-infrared imaging of vegetation for detecting leaking CO2 gas. IEEE J-STARS 7(5), 1651\u20131657 (2014)","DOI":"10.1109\/JSTARS.2013.2295760"},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neucom.2022.09.157","volume":"514","author":"C Panigrahy","year":"2022","unstructured":"Panigrahy, C., Seal, A., Mahato, N.K.: Parameter adaptive unit-linking dual-channel PCNN based infrared and visible image fusion. Neurocomputing 514, 21\u201338 (2022)","journal-title":"Neurocomputing"},{"issue":"4","key":"31_CR3","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s11760-019-01606-1","volume":"14","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Chen, X., Liu, L., Li, Y.F., Deng, Y.B.: A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit. Signal Image Video Process. 14(4), 737\u2013745 (2020)","journal-title":"Signal Image Video Process."},{"key":"31_CR4","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.infrared.2018.07.024","volume":"93","author":"Y Shen","year":"2018","unstructured":"Shen, Y., et al.: Improved Anscombe transformation and total variation for denoising of lowlight infrared images. Infrared Phys. Technol. 93, 192\u2013198 (2018)","journal-title":"Infrared Phys. Technol."},{"key":"31_CR5","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.enganabound.2022.09.019","volume":"145","author":"ZC He","year":"2022","unstructured":"He, Z.C., Wei, B.L., Zhou, L.F., Zhou, E.L., Li, E., Xing, Z.Y.: The crack detection of acoustic metamaterials using a weighted mode shape-wavelet-based strategy. Eng. Anal. Bound. Elements 145, 286\u2013298 (2022)","journal-title":"Eng. Anal. Bound. Elements"},{"issue":"3","key":"31_CR6","first-page":"2199","volume":"37","author":"GR Agah","year":"2022","unstructured":"Agah, G.R., Rahideh, A., Khodadadzadeh, H., Khoshnazar, S.M., Kia, S.H.: Broken rotor bar and rotor eccentricity fault detection in induction motors using a combination of discrete wavelet transform and Teager-Kaiser energy operator. IEEE Trans. Energy Convers. 37(3), 2199\u20132206 (2022)","journal-title":"IEEE Trans. Energy Convers."},{"issue":"3","key":"31_CR7","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613\u2013627 (1995)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"1","key":"31_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.patcog.2004.05.009","volume":"38","author":"GY Chen","year":"2005","unstructured":"Chen, G.Y., Bui, T.D., Krzyzak, A.: Image denoising with neighbour dependency and customized wavelet and threshold. Pattern Recognit. 38(1), 115\u2013124 (2005)","journal-title":"Pattern Recognit."},{"issue":"7","key":"31_CR9","first-page":"826","volume":"24","author":"RL Lu","year":"2004","unstructured":"Lu, R.L., Wu, T.J., Yu, L.: Performance analysis of threshold denoising via different kinds of mother wavelets. Spectroscopy and Spectral Analysis 24(7), 826\u2013829 (2004)","journal-title":"Spectroscopy and Spectral Analysis"},{"issue":"4","key":"31_CR10","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/S1001-6058(08)60077-3","volume":"20","author":"XL Guo","year":"2008","unstructured":"Guo, X.L., Yang, K.L., Guo, Y.X.: Hydraulic pressure signal denoising using threshold self-learning wavelet algorithm. J. Hydrodyn. 20(4), 433\u2013439 (2008)","journal-title":"J. Hydrodyn."},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1109\/LSP.2008.2001815","volume":"15","author":"CB Smith","year":"2008","unstructured":"Smith, C.B., Agaian, S., Akopian, D.: A wavelet-denoising approach using polynomial threshold operators. IEEE Signal Process. Lett. 15, 906\u2013909 (2008)","journal-title":"IEEE Signal Process. Lett."},{"issue":"1","key":"31_CR12","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.dsp.2007.09.006","volume":"18","author":"S Poornachandra","year":"2008","unstructured":"Poornachandra, S.: Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process. 18(1), 49\u201355 (2008)","journal-title":"Digital Signal Process."},{"issue":"28","key":"31_CR13","doi-asserted-by":"publisher","first-page":"8983","DOI":"10.1364\/AO.437674","volume":"60","author":"H Guo","year":"2021","unstructured":"Guo, H., Yue, L.H., Song, P., Tan, Y.M., Zhang, L.J.: Denoising of an ultraviolet light received signal based on improved wavelet transform threshold and threshold function. Appl. Opt. 60(28), 8983\u20138990 (2021)","journal-title":"Appl. Opt."},{"key":"31_CR14","first-page":"6811192","volume":"9","author":"Z Chen","year":"2021","unstructured":"Chen, Z.: Signal recognition for English speech translation based on improved wavelet denoising method. Adv. Math. Phys. 9, 6811192 (2021)","journal-title":"Adv. Math. Phys."},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, N., Lin, P., Xu, L.: Application of weak signal denoising based on improved wavelet threshold. IOP Conf. Ser.: Mater. Sci. Eng. 751(1), 12073 (2020)","DOI":"10.1088\/1757-899X\/751\/1\/012073"},{"key":"31_CR16","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.isatra.2020.12.029","volume":"114","author":"A Kumar","year":"2021","unstructured":"Kumar, A., Tomar, H., Mehla, V.K., Komaragiri, R., Kumar, M.: Stationary wavelet transform based ECG signal denoising method. ISA Trans. 114, 251\u2013262 (2021)","journal-title":"ISA Trans."},{"key":"31_CR17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s13640-018-0401-8","volume":"1","author":"Y Binbin","year":"2019","unstructured":"Binbin, Y.: An improved infrared image processing method based on adaptive threshold denoising. EURASIP J. Image Video Process. 1, 5 (2019)","journal-title":"EURASIP J. Image Video Process."},{"issue":"3","key":"31_CR18","doi-asserted-by":"publisher","first-page":"4081","DOI":"10.1093\/mnras\/stac2774","volume":"517","author":"DC Kim","year":"2022","unstructured":"Kim, D.C., Kim, M., Yoon, I., Momjian, E., Kim, J.H., Letai, J., Evans, A.S.: Adaptive optics and VLBA imaging observations of recoiling supermassive black hole candidates. Monthly Notices Roy. Astron. Soc. 517(3), 4081\u20134091 (2022)","journal-title":"Monthly Notices Roy. Astron. Soc."},{"key":"31_CR19","doi-asserted-by":"publisher","first-page":"103968","DOI":"10.1016\/j.infrared.2021.103968","volume":"119","author":"YY Shao","year":"2021","unstructured":"Shao, Y.Y., et al.: Infrared image stripe noise removing using least squares and gradient domain guided filtering. Infrared Phys. Technol. 119, 103968 (2021)","journal-title":"Infrared Phys. Technol."},{"key":"31_CR20","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.neucom.2019.10.054","volume":"377","author":"JT Guan","year":"2020","unstructured":"Guan, J.T., Lai, R., Xiong, A., Liu, Z.S., Gu, L.: Fixed pattern noise reduction for infrared images based on cascade residual attention CNN. Neurocomputing 377, 301\u2013313 (2020)","journal-title":"Neurocomputing"},{"key":"31_CR21","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.infrared.2018.12.035","volume":"97","author":"HX Jiang","year":"2019","unstructured":"Jiang, H.X., et al.: A resource-efficient parallel architecture for infrared image stripe noise removal based on the most stable window. Infrared Phys. Technol. 97, 258\u2013269 (2019)","journal-title":"Infrared Phys. Technol."},{"key":"31_CR22","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.infrared.2018.04.005","volume":"91","author":"M Jiang","year":"2018","unstructured":"Jiang, M.: Edge enhancement and noise suppression for infrared image based on feature analysis. Infrared Phys. Technol. 91, 142\u2013152 (2018)","journal-title":"Infrared Phys. Technol."},{"key":"31_CR23","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.infrared.2017.08.002","volume":"85","author":"WJ Wang","year":"2017","unstructured":"Wang, W.J., Wei, X.G., Li, J., Wang, G.Y.: Noise suppression algorithm of short-wave infrared star image for daytime star sensor. Infrared Phys. Technol. 85, 382\u2013394 (2017)","journal-title":"Infrared Phys. Technol."},{"key":"31_CR24","first-page":"5003214","volume":"60","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zhou, X., Li, L., Hu, T., Fansheng, C.: A combined stripe noise removal and deblurring recovering method for thermal infrared remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 5003214 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"31_CR25","doi-asserted-by":"publisher","first-page":"7801114","DOI":"10.1109\/JPHOT.2018.2854303","volume":"10","author":"P Xiao","year":"2018","unstructured":"Xiao, P., Guo, Y., Zhuang, P.: Removing stripe noise from infrared cloud images via deep convolutional networks. IEEE Photon. J. 10(4), 7801114 (2018)","journal-title":"IEEE Photon. J."},{"issue":"2","key":"31_CR26","doi-asserted-by":"publisher","first-page":"78006154","DOI":"10.1109\/JPHOT.2017.2779149","volume":"10","author":"X Kuang","year":"2018","unstructured":"Kuang, X., Sui, X., Liu, Y., Chen, Q., Gu, G.: Single infrared image optical noise removal using a deep convolutional neural network. IEEE Photon. J. 10(2), 78006154 (2018)","journal-title":"IEEE Photon. J."},{"issue":"12","key":"31_CR27","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1007\/s11517-019-02014-w","volume":"57","author":"A Bal","year":"2019","unstructured":"Bal, A., Banerjee, M., Sharma, P., Maitra, M.: An efficient wavelet and curvelet-based PET image denoising technique. Med. Biol. Eng. Comput. 57(12), 2567\u20132598 (2019)","journal-title":"Med. Biol. Eng. Comput."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47665-5_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:14:08Z","timestamp":1699128848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47665-5_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031476648","9783031476655"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47665-5_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ericlab.org\/acpr2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","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":"93","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":"57% - 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":"2","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":"5","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)"}}]}}