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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 7 | Page No.: 804-807
DOI: 10.3923/itj.2012.804.807
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ESWL Image Enhancement via Bivariate Wiener Filtering in Undecimated Multiwavelet Domain

Lina Tan and Lianping Zhang

The rapid development of Extracorporeal Shock Wave Lithotripsy (ESWL) due to its non-invasive advantage calls for efficient image processing methods like the enhancement for visual inspection or a preprocessing for clinic diagnosis. In this study, we proposed a bivariate local Wiener filtering algorithm based on Undecimated Multiwavelet Transform (UMWT), where the dynamically generated windows are used for estimation of the signal variances of noisy wavelet coefficients. The computational complexity of the algorithm is low, since the operation of UMWT is the main computational burden, slight more than single wavelet transform. The performance of the new filter is assessed with simulated data experiments and tested with actual ESWL images. The results show that the proposed technique can help to meet the conflicting requirements of reproducing the low-contrast details with suppressing the overwhelming noise.
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  •    TCM and Wavelet De-Noising Over an Improved Algorithm for Channel Estimations of OFDM System Based Pilot Signal
  •    Influence of Noise Distribution on Active Contour Models: Medical Images Segmentation
  •    Interpretation of Ground Penetrating Radar Image Using Digital Wavelet Transform
  •    Application of Homotopy Analysis Method and Variational Iteration Method for Shock Wave Equation
How to cite this article:

Lina Tan and Lianping Zhang, 2012. ESWL Image Enhancement via Bivariate Wiener Filtering in Undecimated Multiwavelet Domain. Information Technology Journal, 11: 804-807.

DOI: 10.3923/itj.2012.804.807






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