{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:18:38Z","timestamp":1781713118671,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005073","name":"Agency for Defense Development","doi-asserted-by":"publisher","award":["202000000100013"],"award-info":[{"award-number":["202000000100013"]}],"id":[{"id":"10.13039\/501100005073","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.<\/jats:p>","DOI":"10.3390\/sym12060938","type":"journal-article","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T03:32:21Z","timestamp":1591327941000},"page":"938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4995-9039","authenticated-orcid":false,"given":"Hyunho","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3759-3116","authenticated-orcid":false,"given":"Jechang","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11075-017-0386-x","article-title":"Speckle noise removal in ultrasound images by first- and second-order total variation","volume":"78","author":"Wang","year":"2018","journal-title":"Numer. Algorithm"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.patrec.2019.12.005","article-title":"Speckle noise suppression in 2D ultrasound kidney images using local pattern based topological derivative","volume":"131","author":"Alex","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11732","DOI":"10.1016\/j.ijleo.2016.09.054","article-title":"Reduction of speckle noise ultrasound images based on TV regularization and modified bayes shrink techniques","volume":"127","author":"Elyasi","year":"2016","journal-title":"Optik"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/42.544498","article-title":"Adaptive speckle reduction filter for log compressed B-scan images","volume":"15","author":"Duff","year":"1996","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sanches, J., Laine, A., and Suri, J. (2012). Ultrasound Imaging: Advances and Applications, Springer.","DOI":"10.1007\/978-1-4614-1180-2"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/S0146-664X(81)80018-4","article-title":"Refined filtering of image noise using local statistics","volume":"15","author":"Lee","year":"1981","journal-title":"Comput. Graph. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1985.4767641","article-title":"Adaptive noise smoothing filter for images with signal-dependent noise","volume":"PAMI-7","author":"Kuan","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TPAMI.1982.4767223","article-title":"A model for radar images and its application to adaptive digital filtering of multiplicative noise","volume":"PAMI-4","author":"Frost","year":"1982","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1109\/36.62623","article-title":"Adaptive speckle filters and scene heterogeneity","volume":"28","author":"Lopes","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/TBME.2002.1028423","article-title":"Real-Time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion","volume":"49","author":"Abd","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.1364\/AO.51.004916","article-title":"Overview of anisotropic filtering methods based on partial differential equations for electronic speckle pattern interferometry","volume":"51","author":"Tang","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1109\/TIP.2009.2024064","article-title":"Nonlocal means-based speckle filtering for ultrasound images","volume":"18","author":"Coupe","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2015.05.140","article-title":"Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image","volume":"195","author":"Yang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Radlak, K., and Smolka, B. (2014). Adaptive non-local means filtering for speckle noise reduction. International Conference on Computer Vision and Graphics, Springer.","DOI":"10.1007\/978-3-319-11331-9_62"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2016.03.001","article-title":"Speckle reduction in medical ultrasound images using an unbiased non-local means method","volume":"28","author":"Sudeep","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.1364\/AO.58.007110","article-title":"Speckle denoising by variant nonlocal means methods","volume":"58","author":"Tounsi","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7681","DOI":"10.1364\/AO.57.007681","article-title":"Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods","volume":"57","author":"Tounsi","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TIP.2017.2685339","article-title":"Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D","volume":"26","author":"Santos","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TIP.2003.818640","article-title":"Image denoising using scale mixtures of Gaussians in the wavelet domain","volume":"12","author":"Portilla","year":"2003","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1109\/TBME.2008.923140","article-title":"Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors","volume":"55","author":"Rabbani","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.image.2016.01.005","article-title":"Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy\u2013Schwarz divergence in wavelet domain","volume":"42","author":"Rami","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neucom.2016.09.011","article-title":"An automated method for sleep staging from EEG signals using normal Gaussian parameters and adaptive boosting","volume":"219","author":"Hassan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1016\/j.patcog.2009.01.005","article-title":"Image denoising in steerable pyramid domain based on a local Laplace prior","volume":"42","author":"Rabbani","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.sigpro.2014.03.028","article-title":"Dual-tree complex wavelet coefficient magnitude modeling using the bivariate Cauchy\u2013Rayleigh distribution","volume":"105","author":"Hill","year":"2014","journal-title":"Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"034109","DOI":"10.1117\/1.OE.58.3.034109","article-title":"Contribution study of monogenic wavelets transform to reduce speckle noise in digital speckle pattern interferometry","volume":"58","author":"Zada","year":"2019","journal-title":"Opt. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23463","DOI":"10.1364\/OE.20.023463","article-title":"Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform","volume":"20","author":"Trusiak","year":"2012","journal-title":"Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TIP.2007.891064","article-title":"A new SURE approach to image denoising intrascale orthonormal wavelet thresholding","volume":"16","author":"Luisier","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TIP.2012.2200491","article-title":"Efficient image denoising method based on a new adaptive wavelet packet thresholding function","volume":"21","author":"Fathi","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.sigpro.2014.01.022","article-title":"A novel image denoising algorithm using linear Bayesian MMSE estimation based on sparse representation","volume":"100","author":"Sun","year":"2014","journal-title":"Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2017.10.006","article-title":"Enhanced Wiener filter for ultrasound image restoration","volume":"153","author":"Baselice","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TIP.2002.804276","article-title":"Speckle reducing anisotropic diffusion","volume":"11","author":"Yu","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1109\/TIP.2015.2468183","article-title":"Gradient domain guided image filtering","volume":"24","author":"Kou","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TIP.2014.2371234","article-title":"Weighted guided image filtering","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TGRS.2002.1000333","article-title":"Statistical properties of logarithmically transformed speckle","volume":"40","author":"Xie","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ultras.2015.10.005","article-title":"Speckle filtering of medical ultrasonic images using wavelet and guided filter","volume":"65","author":"Zhang","year":"2016","journal-title":"Ultrasonics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/T-SU.1983.31404","article-title":"Statistics of speckle in ultrasound B-scans","volume":"30","author":"Wagner","year":"1983","journal-title":"IEEE Trans. Sonics Ultrason."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation via wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thesholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/83.862633","article-title":"Adaptive wavelet thresholding for image denoising and compression","volume":"9","author":"Chang","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.neucom.2008.04.016","article-title":"Image denoising in the wavelet domain using a new adaptive thresholding function","volume":"72","author":"Nasri","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.dsp.2005.01.002","article-title":"Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction","volume":"15","author":"Thakur","year":"2005","journal-title":"Digit. Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TIP.2008.2006658","article-title":"Multiresolution bilateral filtering for image denoising","volume":"17","author":"Zhang","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","first-page":"885","article-title":"Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold","volume":"27","author":"Choi","year":"2019","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1007\/s11277-019-06229-w","article-title":"Wavelet and Total Variation Based Method Using Adaptive Regularization for Speckle Noise Reduction in Ultrasound Images","volume":"106","author":"Rawat","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_46","unstructured":"(2019, April 16). Ultrasouind cases.info. Available online: http:\/\/ultrasoundcases.info\/Category.aspx?cat=117."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TIP.2014.2371244","article-title":"Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images","volume":"24","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360666","article-title":"Edge-preserving decompositions for multi-scale tone and detail manipulation","volume":"27","author":"Farbman","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5199","DOI":"10.1109\/TIP.2016.2605302","article-title":"The Bitonic Filter: Linear Filtering in an Edge-Preserving Morphological Framework","volume":"25","author":"Treece","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TGRS.2011.2161586","article-title":"A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage","volume":"50","author":"Parrilli","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/JSTARS.2014.2375359","article-title":"Patch Ordering-Based SAR Image Despeckling Via Transform-Domain Filtering","volume":"8","author":"Xu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Nair, J.J., and Govindan, V. (2013). Speckle noise reduction using fourth order complex diffusion based homomorphic filter. Advances in Computing and Information Technology, Springer.","DOI":"10.1007\/978-3-642-31552-7_91"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.aci.2017.01.002","article-title":"An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images","volume":"14","author":"Prabusankarlal","year":"2018","journal-title":"Appl. Comput. Inform."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:35:15Z","timestamp":1760175315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,3]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["sym12060938"],"URL":"https:\/\/doi.org\/10.3390\/sym12060938","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,3]]}}}