{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:23:35Z","timestamp":1769822615559,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11042-023-15266-4","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T03:26:43Z","timestamp":1682479603000},"page":"43923-43943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An effective image-denoising method with the integration of thresholding and optimized bilateral filtering"],"prefix":"10.1007","volume":"82","author":[{"given":"B. Chinna","family":"Rao","sequence":"first","affiliation":[]},{"given":"S. Saradha","family":"Rani","sequence":"additional","affiliation":[]},{"given":"K.","family":"Shashidhar","sequence":"additional","affiliation":[]},{"given":"Gandi","family":"Satyanarayana","sequence":"additional","affiliation":[]},{"given":"K.","family":"Raju","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"issue":"3","key":"15266_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/17517575.2018.1557256","volume":"13","author":"UA Bhatti","year":"2019","unstructured":"Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterp Inf Syst 13(3):329\u2013351","journal-title":"Enterp Inf Syst"},{"key":"15266_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","volume":"60","author":"UA Bhatti","year":"2021","unstructured":"Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Nawaz SA, Bhatti MA, Ain QU, Mehmood A (2021) Local similarity-based spatial-spectral fusion hyperspectral image classification with deep CNN and gabor filtering. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"15266_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2020.165864","volume":"225","author":"H Chen","year":"2021","unstructured":"Chen H (2021) Fusion denoising algorithm of optical coherence tomography image based on point-estimated and block-estimated. Optik 225:165864","journal-title":"Optik"},{"key":"15266_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101632","volume":"55","author":"Z Chen","year":"2020","unstructured":"Chen Z, Zeng Z, Shen H, Zheng X, Dai P, Ouyang P (2020) DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images. Biomed Signal Process Control 55:101632","journal-title":"Biomed Signal Process Control"},{"issue":"18","key":"15266_CR5","doi-asserted-by":"publisher","first-page":"5162","DOI":"10.3390\/s20185162","volume":"20","author":"CL Chowdhary","year":"2020","unstructured":"Chowdhary CL, Patel PV, Kathrotia KJ, Attique M, Perumal K, Ijaz MF (2020) Analytical study of hybrid techniques for image encryption and decryption. Sensors 20(18):5162","journal-title":"Sensors"},{"key":"15266_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101754","volume":"57","author":"M Diwakar","year":"2020","unstructured":"Diwakar M, Singh P (2020) CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomed Signal Process Control 57:101754","journal-title":"Biomed Signal Process Control"},{"issue":"21","key":"15266_CR7","doi-asserted-by":"publisher","first-page":"14449","DOI":"10.1007\/s11042-018-6897-1","volume":"79","author":"M Diwakar","year":"2020","unstructured":"Diwakar M, Kumar P, Singh AK (2020) CT image denoising using NLM and its method noise thresholding. Multimed Tools Appl 79(21):14449\u201314464","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"15266_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/080725891","volume":"2","author":"T Goldstein","year":"2009","unstructured":"Goldstein T, Osher S (2009) The split bregman method for l1 regularized problems. SIAM J Imaging Sci 2(2):323\u2013334","journal-title":"SIAM J Imaging Sci"},{"key":"15266_CR9","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.sigpro.2019.06.031","volume":"165","author":"C Gong","year":"2019","unstructured":"Gong C, Zeng L (2019) Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography. Signal Process 165:149\u2013162","journal-title":"Signal Process"},{"issue":"23","key":"15266_CR10","doi-asserted-by":"publisher","first-page":"4941","DOI":"10.3390\/rs13234941","volume":"13","author":"R Hussain","year":"2021","unstructured":"Hussain R, Karbhari Y, Ijaz MF, Wo\u017aniak M, Singh PK, Sarkar R (2021) Revise-Net: exploiting reverse attention mechanism for salient object detection. Remote Sens 13(23):4941","journal-title":"Remote Sens"},{"issue":"6","key":"15266_CR11","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.3390\/ijerph18063056","volume":"18","author":"M Irfan","year":"2021","unstructured":"Irfan M, Iftikhar MA, Yasin S, Draz U, Ali T, Hussain S, Bukhari S, Alwadie AS, Rahman S, Glowacz A, Althobiani F (2021) Role of hybrid deep neural networks (HDNNs), computed tomography, and chest X-rays for the detection of COVID-19. Int J Environ Res Public Health 18(6):3056","journal-title":"Int J Environ Res Public Health"},{"key":"15266_CR12","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.softx.2019.04.003","volume":"9","author":"D Kazantsev","year":"2019","unstructured":"Kazantsev D, Pasca E, Turner MJ, Withers PJ (2019) CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX 9:317\u2013323","journal-title":"SoftwareX"},{"key":"15266_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100573","volume":"24","author":"G Khaleghi","year":"2021","unstructured":"Khaleghi G, Hosntalab M, Sadeghi M, Reiazi R, Mahdavi SR (2021) Metal artifact reduction in computed tomography images based on developed generative adversarial neural network. Inform Med Unlocked 24:100573","journal-title":"Inform Med Unlocked"},{"key":"15266_CR14","doi-asserted-by":"crossref","unstructured":"Li J, Yu J, Xu L, Xue X, Chang CC, Mao X, Hu J (2018) A cascaded algorithm for image quality assessment and image denoising based on CNN for image security and authorization.\u00a0Secur Commun Netw","DOI":"10.1155\/2018\/8176984"},{"key":"15266_CR15","doi-asserted-by":"crossref","unstructured":"Liu Y, Tang S (2022) Artificial intelligence algorithm-based computed tomography image of both kidneys in diagnosis of renal dysplasia.\u00a0Comput Math Methods Med","DOI":"10.1155\/2022\/5823720"},{"key":"15266_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jngse.2020.103227","volume":"77","author":"B Mahanta","year":"2020","unstructured":"Mahanta B, Vishal V, Ranjith PG, Singh TN (2020) An insight into pore-network models of high-temperature heat-treated sandstones using computed tomography. J Nat Gas Sci Eng 77:103227","journal-title":"J Nat Gas Sci Eng"},{"key":"15266_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.dendro.2021.125877","volume":"69","author":"J Martinez-Garcia","year":"2021","unstructured":"Martinez-Garcia J, Stelzner I, Stelzner J, Gwerder D, Schuetz P (2021) Automated 3D tree-ring detection and measurement from X-ray computed tomography. Dendrochronologia 69:125877","journal-title":"Dendrochronologia"},{"issue":"1","key":"15266_CR18","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s11277-020-07732-1","volume":"116","author":"A Naik","year":"2021","unstructured":"Naik A, Edla DR (2021) Lung nodule classification on computed tomography images using deep learning. Wireless Pers Commun 116(1):655\u2013690","journal-title":"Wireless Pers Commun"},{"key":"15266_CR19","doi-asserted-by":"crossref","unstructured":"Ojha C, Fusco A, Manunta M (2015) "Denoising of full resolution differential SAR interferogram based on K-SVD technique.&quot. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp 2461\u20132464","DOI":"10.1109\/IGARSS.2015.7326308"},{"key":"15266_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109267","volume":"132","author":"RP Reimer","year":"2020","unstructured":"Reimer RP, Salem J, Merkt M, Sonnabend K, Lennartz S, Zopfs D, Heidenreich A, Maintz D, Haneder S, Hokamp NG (2020) Size and volume of kidney stones in computed tomography: influence of acquisition techniques and image reconstruction parameters. Eur J Radiol 132:109267","journal-title":"Eur J Radiol"},{"issue":"6","key":"15266_CR21","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1007\/s11760-012-0389-y","volume":"7","author":"BK Shreyamsha Kumar","year":"2013","unstructured":"Shreyamsha Kumar BK (2013) Image denoising based on non-local means filter and its method noise thresholding. Springer J Sig Image Video Process 7(6):1211\u20131227","journal-title":"Springer J Sig Image Video Process"},{"key":"15266_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2021.104716","volume":"151","author":"M Sidorenko","year":"2021","unstructured":"Sidorenko M, Orlov D, Ebadi M, Koroteev D (2021) Deep learning in denoising of micro-computed tomography images of rock samples. Comput Geosci 151:104716","journal-title":"Comput Geosci"},{"issue":"3","key":"15266_CR23","first-page":"3303","volume":"69","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, Ahmed S, Alhumam A, Kumar AB, Ijaz MF (2021) An AW-HARIS based automated segmentation of human liver using CT images. Comput Mater Contin 69(3):3303\u20133319","journal-title":"Comput Mater Contin"},{"key":"15266_CR24","doi-asserted-by":"publisher","first-page":"18762","DOI":"10.1109\/ACCESS.2021.3054250","volume":"9","author":"J Tamang","year":"2021","unstructured":"Tamang J, Nkapkop JDD, Ijaz MF, Prasad PK, Tsafack N, Saha A, Kengne J, Son Y (2021) Dynamical properties of ion-acoustic waves in space plasma and its application to image encryption. IEEE Access 9:18762\u201318782","journal-title":"IEEE Access"},{"key":"15266_CR25","doi-asserted-by":"crossref","unstructured":"Tan S, Xu Z (2022) Intelligent algorithm-based multislice spiral computed tomography to diagnose coronary heart disease.\u00a0Comput Math Methods Med","DOI":"10.1155\/2022\/4900803"},{"issue":"11","key":"15266_CR26","doi-asserted-by":"publisher","first-page":"2778","DOI":"10.1109\/TIP.2007.906002","volume":"16","author":"B Thierry","year":"2007","unstructured":"Thierry B, Florian L (2007) The SURE-LET approach to image denoising. IEEE Trans Image Process 16(11):2778\u20132786","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"15266_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42492-021-00087-9","volume":"4","author":"K Usui","year":"2021","unstructured":"Usui K, Ogawa K, Goto M, Sakano Y, Kyougoku S, Daida H (2021) Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. Vis Comput Ind Biomed Art 4(1):1\u20139","journal-title":"Vis Comput Ind Biomed Art"},{"key":"15266_CR28","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neucom.2019.09.087","volume":"378","author":"F Yang","year":"2020","unstructured":"Yang F, Zhang D, Zhang H, Huang K, Du Y, Teng M (2020) Streaking artifacts suppression for cone-beam computed tomography with the residual learning in neural network. Neurocomputing 378:65\u201378","journal-title":"Neurocomputing"},{"key":"15266_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compscitech.2021.108875","volume":"213","author":"H Yang","year":"2021","unstructured":"Yang H, Wang W, Shang J, Wang P, Lei H, Chen HS, Fang D (2021) Segmentation of computed tomography images and high-precision reconstruction of rubber composite structure based on deep learning. Compos Sci Technol 213:108875","journal-title":"Compos Sci Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15266-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15266-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15266-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:07:01Z","timestamp":1698473221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15266-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,26]]},"references-count":29,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["15266"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15266-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,26]]},"assertion":[{"value":"14 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}