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A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets

Abstract

Multiwavelets, wavelets with several scaling functions, offer simultaneous orthogonality, symmetry, and short support, which is not possible with ordinary (scalar) wavelets. These properties make multiwavelets promising for signal processing applications, such as image denoising. The common approach for image denoising is to get the multiwavelet decomposition of a noisy image and apply a common threshold to each coefficient separately. This approach does not generally give sufficient performance. In this paper, we propose a multivariate thresholding technique for image denoising with multiwavelets. The proposed technique is based on the idea of restoring the spatial dependence of the pixels of the noisy image that has undergone a multiwavelet decomposition. Coefficients with high correlation are regarded as elements of a vector and are subject to a common thresholding operation. Simulations with several multiwavelets illustrate that the proposed technique results in a better performance.

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Correspondence to Erdem Bala.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Bala, E., Ertüzün, A. A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets. EURASIP J. Adv. Signal Process. 2005, 297296 (2005). https://doi.org/10.1155/ASP.2005.1205

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Keywords and phrases

  • multiwavelets
  • image denoising
  • multivariate thresholding