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Sven-Erik Hjelt. Stefano Rovetta. System Identification. Karel J. Miguel Aranda. Computer Solution of Large Linear Systems. This paper presents the image de-noising on Recently, a new member of the emerging different Axial-transverse MRI using wavelet representation family of multiscale geometric transform, curvelet transform and contourlet transform.

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Moreover, the contourlets transform has been efficiently used in medical image denoising [2], and obtained higher. Figure 1. Curvelet Spatial decomposition III. Contourlet transform has better performances in representing the image such as lines, edges, contours and curves than wavelet and curvelet transform because of its directionality and anisotropy. The contourlet transform consists of two steps which is the sub band decomposition and the directional transform [4].

A Laplacian pyramid is first used to capture point discontinuities [14], then followed by directional filter Figure 2. The Contourlet transform framework [8] banks to link point discontinuity into lineal structure.

The overall result is an image expansion using basic elements This figure shows a flow diagram of the contourlet like contour segments, [9] thus the term contourlet transform transform being coined [5] [10]. For instance, the 2 dimensional algorithms are based on separate variables leading to prioritizing of vertical, horizontal and diagonal directions. The Contourlet filter bank [6]. Various types of noise like the Random noise, Figure 4. The directional filter bank Gaussian noise, Salt, Pepper and speckle noise were added to this image.

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Wavelet transforms always incorporate a result in a 0 and variance is 0. The PSNR is the most commonly used as a measure defined on a finite and limited domain.

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The Wavelets also usually allow an exact reconstitution of PSNR for both noisy and denoised images were original data. A sufficient condition for this in the identified using the following formula: continuous case wavelet transform is the wavelet 2 coefficients and which allow reconstitution, are of zero Mean Square Error MSE which requires two m x n mean. Inner products with the wavelets can be performed in the original domain or 3 in the Fourier domain; the former is preferred when the Here, MAXI is the maximum pixel value of the support of the wavelet function is small i.

The PSNR of the images denoised is compared on a limited number of grid points. The discrete Wavelet using wavelet, curvelet and contourlet transform for each Transform DWT can be derived from this theorem if type of noise mentioned above.

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Then the mean and the signal under consideration is band limited. If we standard deviation of each noise was calculated. The original image Figure 8. Denoising of an axial-transverse image of patient 1 with Speckle noise Figure 6. Denoising of an axial-transverse image of patient 1 with Random noise. Figure 9.

## Jalal Fadili - Google Scholar Citations

Denoising of an axial-transverse image of patient 1 with Gaussian noise Figure 7. The visual quality of Identifier: And the texture [6] Masood, H. Digital Object Identifier: ICIEA Identifier: CiSE