{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:25:52Z","timestamp":1773246352208,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T00:00:00Z","timestamp":1487808000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61379001"],"award-info":[{"award-number":["61379001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1007\/s00034-017-0496-7","type":"journal-article","created":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T06:01:21Z","timestamp":1487829681000},"page":"4006-4021","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An Improved Denoising Model Based on the Analysis K-SVD Algorithm"],"prefix":"10.1007","volume":"36","author":[{"given":"Wenru","family":"Gong","sequence":"first","affiliation":[]},{"given":"Hongyi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Di","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,2,23]]},"reference":[{"key":"496_CR1","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"M. Aharon, M. Elad, A. Bruckstein, SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311\u20134322 (2006). doi: 10.1109\/TSP.2006.881199","journal-title":"IEEE Trans. Signal Process."},{"key":"496_CR2","doi-asserted-by":"publisher","first-page":"046206","DOI":"10.1103\/PhysRevE.86.046206","volume":"86","author":"RG Andrzejak","year":"2012","unstructured":"R.G. Andrzejak, K. Schindler, C. Rummel, Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 86, 046206 (2012). doi: 10.1103\/PhysRevE.86.046206","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"496_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1137\/060657704","volume":"51","author":"AM Bruckstein","year":"2009","unstructured":"A.M. Bruckstein, D.L. Donoho, M. Elad, From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51, 34\u201381 (2009). doi: 10.1137\/060657704","journal-title":"SIAM Rev."},{"key":"496_CR4","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1137\/090753504","volume":"8","author":"JF Cai","year":"2010","unstructured":"J.F. Cai, S. Osher, Z. Shen, Split Bregman methods and frame based image restoration. Multiscale Model. Simul. 8, 337\u2013369 (2010). doi: 10.1137\/090753504","journal-title":"Multiscale Model. Simul."},{"key":"496_CR5","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/TIP.2014.2299065","volume":"23","author":"Y Chen","year":"2014","unstructured":"Y. Chen, R. Ranftl, T. Pock, Insights into analysis operator learning: a view from higher-order filter-based mrf model. IEEE Trans. Image Process. 23, 1060\u20131072 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"496_CR6","doi-asserted-by":"crossref","first-page":"4741","DOI":"10.1109\/TIP.2015.2466117","volume":"24","author":"J Chen","year":"2015","unstructured":"J. Chen, Z. Zhang, Y. Wang, Relevance metric learning for person re-identification by exploiting listwise similarities. IEEE Trans. Image Proccess. 24, 4741\u20134755 (2015)","journal-title":"IEEE Trans. Image Proccess."},{"key":"496_CR7","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"D.L. Donoho, De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41, 613\u2013627 (1995). doi: 10.1109\/18.382009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"496_CR8","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15, 3736\u20133745 (2006). doi: 10.1109\/TIP.2006.881969","journal-title":"IEEE Trans. Image Process."},{"key":"496_CR9","unstructured":"J.M. Fadili, G. Peyr, Learning analysis sparsity priors, in Sampta\u201911 (2011)"},{"key":"496_CR10","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.ymssp.2015.08.012","volume":"70\u201371","author":"R Golafshan","year":"2016","unstructured":"R. Golafshan, K. Yuce Sanliturk, SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults. Mech. Syst. Signal Process. 70\u201371, 36\u201350 (2016). doi: 10.1016\/j.ymssp.2015.08.012","journal-title":"Mech. Syst. Signal Process."},{"key":"496_CR11","doi-asserted-by":"crossref","unstructured":"M. Gay, L. Lampe, A. Lampe, SVD-based de-noising and parametric channel estimation for power line communication systems, in International Symposium on Power Line Communications and Its Applications, pp. 7\u201312 (2016)","DOI":"10.1109\/ISPLC.2016.7476272"},{"key":"496_CR12","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.patcog.2016.03.001","volume":"57","author":"H Hao","year":"2016","unstructured":"H. Hao, Q. Wang, P. Li, L. Zhang, Evaluation of ground distances and features in EMD-based GMM matching for texture classification. Pattern Recognit. 57, 152\u2013163 (2016). doi: 10.1016\/j.patcog.2016.03.001","journal-title":"Pattern Recognit."},{"key":"496_CR13","doi-asserted-by":"crossref","unstructured":"J. Jenitta, A. Rajeswari, Denoising of ECG signal based on improved adaptive filter with EMD and EEMD, in IEEE Conference on Information and Communication Technologies, pp. 957\u2013962 (2013)","DOI":"10.1109\/CICT.2013.6558234"},{"key":"496_CR14","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TAC.1980.1102314","volume":"25","author":"V Klema","year":"1980","unstructured":"V. Klema, A. Laub, The singular value decomposition: its computation and some applications. IEEE Trans. Autom. Control 25, 164\u2013176 (1980). doi: 10.1109\/TAC.1980.1102314","journal-title":"IEEE Trans. Autom. Control"},{"key":"496_CR15","doi-asserted-by":"crossref","unstructured":"Z. Lai, Z. Jin, J. Yang, Global sparse representation projections for feature extraction and classification, in IEEE Chinese Conference on Pattern Recognition, pp. 1\u20135 (2009)","DOI":"10.1109\/CCPR.2009.5344136"},{"key":"496_CR16","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.dsp.2015.09.011","volume":"48","author":"Y Li","year":"2016","unstructured":"Y. Li, S. Ding, Z. Li, Dictionary learning with the cosparse analysis model based on summation of blocked determinants as the sparseness measure. Digit. Signal Process. 48, 298\u2013309 (2016). doi: 10.1016\/j.dsp.2015.09.011","journal-title":"Digit. Signal Process."},{"key":"496_CR17","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2016.0307","author":"H Li","year":"2016","unstructured":"H. Li, L. Li, D. Zhao, J. Chen, P. Wang, Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz-based singular value decomposition. IET Signal Process. (2016). doi: 10.1049\/iet-spr.2016.0307","journal-title":"IET Signal Process."},{"key":"496_CR18","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/150127","author":"H Li","year":"2015","unstructured":"H. Li, C. Wang, D. Zhao, An improved EMD and its applications to find the basis functions of EMI signals. Math. Probl. Eng. (2015). doi: 10.1155\/2015\/150127","journal-title":"Math. Probl. Eng."},{"key":"496_CR19","first-page":"4962","volume":"218","author":"HY Li","year":"2012","unstructured":"H.Y. Li, D. Zhao, F. Dai, D.L. Su, On the spectral radius of a nonnegative centrosymmetric matrix. Appl. Math. Comput. 218, 4962\u20134966 (2012)","journal-title":"Appl. Math. Comput."},{"key":"496_CR20","volume-title":"A Wavelet Tour of Signal Processing","author":"S Mallat","year":"1998","unstructured":"S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, San Diego, 1998)"},{"key":"496_CR21","volume-title":"A Wavelet Tour of Signal Processing","author":"S Mallat","year":"1999","unstructured":"S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, San Diego, 1999)"},{"key":"496_CR22","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"SG Mallat","year":"1993","unstructured":"S.G. Mallat, Z. Zhang, Matching pursuits with time\u2013frequency dictionaries. IEEE Trans. Signal Process. 41, 3397\u20133415 (1993). doi: 10.1109\/78.258082","journal-title":"IEEE Trans. Signal Process."},{"key":"496_CR23","doi-asserted-by":"crossref","unstructured":"S. Nam, M.E. Davies, M. Elad, Cosparse analysis modeling-uniqueness and algorithms, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5804\u20135807 (2011)","DOI":"10.1109\/ICASSP.2011.5947680"},{"key":"496_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.acha.2012.03.006","volume":"34","author":"S Nam","year":"2013","unstructured":"S. Nam, M.E. Davies, M. Elad, R. Gribonval, The cosparse analysis model and algorithms. Appl. Comput. Harmonic Anal. 34, 30\u201356 (2013). doi: 10.1016\/j.acha.2012.03.006","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"496_CR25","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TNSRE.2015.2454503","volume":"24","author":"GR Naik","year":"2016","unstructured":"G.R. Naik, S.E. Selvan, H.T. Nguyen, Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 734\u2013743 (2016). doi: 10.1109\/TNSRE.2015.2454503","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"496_CR26","unstructured":"B. Ophir, M. Elad, N. Bertin, Sequential minimal eigenvalues\u2014an approach to analysis dictionary learning, in 19th European Signal Processing Conference, pp. 1465\u20131469 (2011)"},{"key":"496_CR27","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TIT.2012.2226924","volume":"59","author":"T Peleg","year":"2012","unstructured":"T. Peleg, M. Elad, Performance guarantees of the thresholding algorithm for the co-sparse analysis model. IEEE Trans. Inf. Theory 59, 1832\u20131845 (2012)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"496_CR28","doi-asserted-by":"crossref","unstructured":"Y. Romano, M. Elad, Patch-disagreement as a way to improve K-SVD denoising, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1280\u20131284 (2015)","DOI":"10.1109\/ICASSP.2015.7178176"},{"key":"496_CR29","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TSP.2012.2226445","volume":"61","author":"R Rubinstein","year":"2013","unstructured":"R. Rubinstein, T. Peleg, M. Elad, K-SVD: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans. Signal Process. 61, 661\u2013677 (2013)","journal-title":"IEEE Trans. Signal Process."},{"key":"496_CR30","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511730344","volume-title":"Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity","author":"JL Starck","year":"2010","unstructured":"J.L. Starck, F. Murtagh, J.M. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Cambridge University Press, Cambridge, 2010)"},{"key":"496_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"Z Wu","year":"2009","unstructured":"Z. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1\u201341 (2009). doi: 10.1142\/S1793536909000047","journal-title":"Adv. Adapt. Data Anal."},{"key":"496_CR32","unstructured":"M. Yaghoobi, M.E. Davies, Relaxed analysis operator learning, in Proceedings of the NIPS, Workshop on Analysis Operator Learning vs. Dictionary Learning: Fraternal Twins in Sparse Modeling (2012)"},{"key":"496_CR33","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1109\/TSP.2013.2250968","volume":"61","author":"M Yaghoobi","year":"2013","unstructured":"M. Yaghoobi, S. Nam, R. Gribonval, M.E. Davies, Constrained overcomplete analysis operator learning for cosparse signal modelling. IEEE Trans. Signal Process. 61, 2341\u20132355 (2013). doi: 10.1109\/TSP.2013.2250968","journal-title":"IEEE Trans. Signal Process."},{"key":"496_CR34","unstructured":"X. Zha, S. Ni, C. Xie, Self-assisting determination of effective rank degree in SVD denoising, in Application Research of Computers, pp. 1359\u20131362 (2016)"},{"key":"496_CR35","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s12204-015-1691-y","volume":"21","author":"H Zhu","year":"2016","unstructured":"H. Zhu, C. Wang, H. Chen, J. Wang, Pulsed eddy current signal denoising based on singular value decomposition. J. Shanghai Jiaotong Univ. (Sci.) 21, 121\u2013128 (2016). doi: 10.1007\/s12204-015-1691-y","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"key":"496_CR36","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jsv.2016.01.046","volume":"370","author":"M \u017dvokelj","year":"2016","unstructured":"M. \u017dvokelj, S. Zupan, I. Prebil, EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J. Sound Vib. 370, 394\u2013423 (2016). doi: 10.1016\/j.jsv.2016.01.046","journal-title":"J. Sound Vib."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00034-017-0496-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-017-0496-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-017-0496-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T20:45:07Z","timestamp":1568839507000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00034-017-0496-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,23]]},"references-count":36,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2017,10]]}},"alternative-id":["496"],"URL":"https:\/\/doi.org\/10.1007\/s00034-017-0496-7","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,23]]}}}