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Image Denoising using Wavelet Thresholding Techniques

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Image Denoising using Wavelet Thresholding Techniques. Submitted by Yang Yang 9024553282. Introduction. Image denoising: Removing unwanted noise in order to restore the original image. Wavelet transform provides us with one of the methods for image denoising. - PowerPoint PPT Presentation

Text of Image Denoising using Wavelet Thresholding Techniques

  • Image Denoising using Wavelet Thresholding TechniquesSubmitted byYang Yang9024553282

  • IntroductionImage denoising: Removing unwanted noise in order to restore the original image.Wavelet transform provides us with one of the methods for image denoising.Wavelet transform, due to its excellent localization property, has rapidly become an indispensable signal and image processing tool for a variety of applications, including denoising and compression.Wavelet denoising attempts to remove the noise present in the signal while preserving the signal characteristics, regardless of its frequency content.

  • IntroductionIt involves three steps: a linear forward wavelet transformnonlinear thresholding step and a linear inverse wavelet transform Methods UsedUniversal ThresholdingVisu ShrinkSure ShrinkBayes Shrink

  • Wavelet ThresholdingWavelet thresholding (first proposed by Donoho) is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. It removes noise by killing coefficients that are insignificant relative to some threshold.Researchers have developed various techniques for choosing denoising parameters and so far there is no best universal threshold determination technique.TypesUniversal or Global ThresholdingHardSoftSubBand Adaptive Thresholding

  • Hard & Soft ThresholdingThe hard thresholding operator is defined asD(U, ) = U for all |U|>

    Hard threshold is a keep or kill procedure and is more intuitively appealing.The transfer function of the same is shown here.The soft thresholding operator is defined as

    D(U, ) = sgn(U)max(0, |U| - )

    Soft thresholding shrinks coefficients above the threshold in absolute value. The transfer function of the same is shown here.

  • Universal or Global ThresholdingThe threshold

    (N being the signal length, being the noise variance) is well known in wavelet literature as the Universal threshold. It is the optimal threshold in the asymptotic sense and minimizes the cost function of the difference between the function and the soft thresholded version of the same in the L2 norm sense.It is useful for obtain a starting value when nothing is known of the signal condition.

  • Results with Hard & Soft Thresholds (Universal thresholding)

  • VisuShrinkVisuShrink is thresholding by applying the Universal threshold proposed by Donoho and Johnstone. This threshold is given by

    where is the noise variance and M is the number of pixels in the image. For denoising images, VisuShrink is found to yield an overly smoothed estimate.

  • Results with Visu Shrink

  • SURE ShrinkSUREShrink is a thresholding by applying subband adaptive threshold.It is based on Steins Unbiased Estimator for Risk (SURE), a method for estimating the loss in an unbiased fashion.Let wavelet coefficients in the jth subband be { Xi : i =1,,d }For the soft threshold estimator

    we have

    Select threshold tS by

  • Bayes ShrinkBayesShrink is an adaptive data-driven threshold for image denoising via wavelet soft-thresholding. We assume generalized Gaussian distribution (GGD) for the wavelet coefficients in each detail subband.We then try to find the threshold T which minimizes the Bayesian Risk.

  • Results with SURE & Bayes Shrink

  • Comparison based on minimum MSE

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