- -

Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Rodríguez-Hernández, Miguel A. es_ES
dc.contributor.author San Emeterio, Jose L. es_ES
dc.date.accessioned 2017-07-10T11:41:09Z
dc.date.available 2017-07-10T11:41:09Z
dc.date.issued 2016-03
dc.identifier.issn 1863-1703
dc.identifier.uri http://hdl.handle.net/10251/84849
dc.description.abstract Cycle spinning (CS) and a'trous algorithms are different implementations of the undecimated wavelet transform (UWT). Both algorithms can be used for UWT and even though the resulting wavelet coefficients are different, they keep a correspondence. This paper describes an analysis of the CS algorithm performed in the z-transform domain, showing the similarities and differences with the a'trous implementation. CS generates more wavelet coefficients than a'trous, but the number of significative and different coefficients is the same in both cases because of the occurrence of a periodic repetition in CS coefficients. Mathematical expressions for the relationship between CS and a'trous coefficients and for CS coefficient periodicities are provided in the z-transform domain. In some wavelet denoising applications, periodicities (present in the coefficients of the CS procedure) can also be found in the performance measure of the processed signals. In particular, in ultrasonic CS denoising applications, periodicities have been appreciated in the signal-to-noise ratio (SNR) of the ultrasonic denoised signals. These periodicities can be used to optimize the number of CS coefficients for an efficient implementation. Two examples showing the periodicities in the SNR are included. A selection of several reduced sets of CS wavelet coefficients has been utilized in the examples, and the SNRs resulting after denoising are analyzed. es_ES
dc.description.sponsorship This work was partially supported by Spanish MCI Project DPI2011-22438 and MEC Project TIN2013-47272-C2-1-R. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Signal, Image and Video Processing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Wavelets es_ES
dc.subject Cycle spinning es_ES
dc.subject Periodicities es_ES
dc.subject Signal denoising es_ES
dc.subject Ultrasonics es_ES
dc.subject Z-transform es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11760-015-0762-8
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-22438/ES/NUEVAS TECNICAS ULTRASONICAS PARA ESTIMACION NO-INVASIVA. APLICACIONES INNOVADORAS EN TEJIDOS, VEGETALES, MATERIALES MICRO%2FNANOESTRUCTURADOS Y ELEMENTOS ESTRATEGICOS./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-47272-C2-1-R/ES/PLATAFORMA DE SERVICIOS PARA CIUDADES INTELIGENTES CON REDES M2M DENSAS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.description.bibliographicCitation Rodríguez-Hernández, MA.; San Emeterio, JL. (2016). Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain. Signal, Image and Video Processing. 10(3):519-526. https://doi.org/10.1007/s11760-015-0762-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s11760-015-0762-8 es_ES
dc.description.upvformatpinicio 519 es_ES
dc.description.upvformatpfin 526 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 301468 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992) es_ES
dc.description.references Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1999) es_ES
dc.description.references Kovacevic, J., Goyal, V.K., Vetterli, M.: Signal Processing Fourier and Wavelet Representations. http://www.fourierandwavelets.org/SPFWR_a3.1_2012.pdf (2012) es_ES
dc.description.references Burrus, C.S., Gopinath, R.A., Guo, H.: Introduction to Wavelets and Wavelet Transforms. Prentice-Hall, New Jersey (1998) es_ES
dc.description.references Kamilov, U., Bostan, E., Unser, M.: Wavelet shrinkage with consistent cycle spinning generalizes total variation denoising. IEEE Signal Process. Lett. 19(4), 187–190 (2012) es_ES
dc.description.references Kumar, B.K.S.: Image denoising based on non-local means filter and its method noise thresholding. Signal Image Video Process. 7, 1211–1227 (2013) es_ES
dc.description.references Rezazadeh, S., Coulombe, S.: A novel discrete wavelet transform framework for full reference image quality assessment. Signal Image Video Process. 7, 559–573 (2013) es_ES
dc.description.references Atto, A.M., Pastor, D., Mercier, G.: Wavelet shrinkage: unification of basic thresholding functions and thresholds. Signal Image Video Process. 5, 11–28 (2011) es_ES
dc.description.references Yektaii, M., Ahmad, M.O., Bhattacharya, P.: A method for preserving the classifiability of digital images after performing a wavelet-based compression. Signal Image Video Process. 8, 169–180 (2014) es_ES
dc.description.references Kanumuri, T., Dewal, M.L., Anand, R.S.: Progressive medical image coding using binary wavelet transforms. Signal Image Video Process. 8, 883–899 (2014) es_ES
dc.description.references Kubinyi, M., Kreibich, O., Neuzil, J., Smid, R.: EMAT noise suppression using information fusion in stationary wavelet packets. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58, 1027–1036 (2011) es_ES
dc.description.references Abbate, A., Koay, J., Frankel, J., Schroeder, S.C., Das, P.: Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44, 14–26 (1997) es_ES
dc.description.references Pardo, E., San Emeterio, J.L., Rodriguez, M.A., Ramos, A.: Noise reduction in ultrasonic NDT using undecimated wavelet transforms. Ultrasonics 44, e1063–e1067 (2006) es_ES
dc.description.references Pardo, E., Emeterio, J.L., Rodriguez, M.A., Ramos, A.: Shift invariant wavelet denoising of ultrasonic traces. Acta Acust. United Acust. 94, 685–693 (2008) es_ES
dc.description.references Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40, 2464–2482 (1992) es_ES
dc.description.references Coifman, R., Donoho, D.: Translation invariant de-noising. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics, Lecture Notes in Statistics, pp. 125–150. Springer, Berlin (1995) es_ES
dc.description.references Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 2, 674–693 (1989) es_ES
dc.description.references Beylkin, G., Coifman, R., Rokhlin, V.: Fast wavelet transforms and numerical algorithms. Commun. Pure Appl. Math. 44, 141–183 (1991) es_ES
dc.description.references Beylkin, G.: On the representation of operators in bases of compactly supported wavelets. SIAM J. Numer. Anal. 6(6), 1716–1740 (1992) es_ES
dc.description.references Vaidyanathan, P.P.: Multirate Systems and Filter Banks. Prentice Hall, Englewood Cliffs (1992) es_ES
dc.description.references Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994) es_ES
dc.description.references Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90, 1200–1224 (1995) es_ES
dc.description.references Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D.: Wavelet shrinkage: Asymptotia? J. R. Stat. Soc. Ser. B 57, 301–369 (1995) es_ES
dc.description.references Karpur, P., Shankar, P.M., Rose, J.L., Newhouse, V.L.: Split spectrum processing: optimizing the processing parameters using minimization. Ultrasonics 25, 204–208 (1997) es_ES
dc.description.references Lazaro, J.C., San Emeterio, J.L., Ramos, A., Fernandez, J.L.: Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets. Ultrasonics 40, 263–267 (2002) es_ES
dc.description.references Donoho, D.L.: De-noising by soft thresholding. IEEE Trans. Inf. Theory 41, 613–627 (1995) es_ES
dc.description.references Johnstone, I.M., Silverman, B.W.: Wavelet threshold estimators for data with correlated noise. J. R. Stat. Soc. 59, 319–351 (1997) es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem