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

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.

[1]  Prabir Bhattacharya,et al.  A method for preserving the classifiability of digital images after performing a wavelet-based compression , 2014, Signal Image Video Process..

[2]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[3]  Abdourrahmane M. Atto,et al.  Wavelet shrinkage: unification of basic thresholding functions and thresholds , 2011, Signal Image Video Process..

[4]  Michael Unser,et al.  Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising , 2012, IEEE Signal Processing Letters.

[5]  M. L. Dewal,et al.  Progressive medical image coding using binary wavelet transforms , 2014, Signal Image Video Process..

[6]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[7]  A. Abbate,et al.  Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  B. K. Shreyamsha Kumar Image denoising based on non-local means filter and its method noise thresholding , 2013 .

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  J. C. Lázaro,et al.  Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets. , 2002, Ultrasonics.

[11]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[12]  S. Mallat A wavelet tour of signal processing , 1998 .

[13]  R. Coifman,et al.  Fast wavelet transforms and numerical algorithms I , 1991 .

[14]  I. Johnstone,et al.  Wavelet Threshold Estimators for Data with Correlated Noise , 1997 .

[15]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[16]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[17]  Joseph L. Rose,et al.  Split spectrum processing: optimizing the processing parameters using minimization , 1987 .

[18]  G. Beylkin On the representation of operators in bases of compactly supported wavelets , 1992 .

[19]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[20]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[21]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[22]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[23]  E. Pardo,et al.  Shift Invariant Wavelet Denoising of Ultrasonic Traces , 2008 .

[24]  Stéphane Coulombe,et al.  A novel discrete wavelet transform framework for full reference image quality assessment , 2013, Signal Image Video Process..

[25]  G. MallatS. A Theory for Multiresolution Signal Decomposition , 1989 .

[26]  R. Smid,et al.  EMAT noise suppression using information fusion in stationary wavelet packets , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[27]  P. Vaidyanathan Multirate Systems And Filter Banks , 1992 .

[28]  A Ramos,et al.  Noise reduction in ultrasonic NDT using undecimated wavelet transforms. , 2006, Ultrasonics.