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Image denoising using least squares support vector machine

15

Description

Link of paper :

http://www.sciencedirect.com/science/article/pii/S0952197609001377

 

Due to the imperfection of image acquisition systems and transmission channels, images are often corrupted by noise. This degradation leads to a significant reduction of image quality and then makes more difficult to perform high-level vision tasks such as recognition, 3-D reconstruction, or scene interpretation. The image denoising is important, not only because of the evident applications it serves. Being the simplest possible inverse problem, it provides a convenient platform over which image processing ideas and techniques can be assessed.  In most cases, this corruption is commonly modeled by a zero-mean additive white Gaussian random noise.

Over the last decade, plenty of image-denoising methods exist, originating from various disciplines such as probability theory, statistics, partial differential equations, linear and nonlinear filtering, and spectral and multiresolution analysis (Liu et al., 2008). All these methods rely on some explicit or implicit assumptions about the true (noise-free) signal in order to separate it properly from the random noise. In particular, the transform- domain denoising methods typically assume that the true signal can be well approximated by a linear combination of few basis elements (Dabov et al., 2007). That is, the signal is sparsely represented in the transform domain. Hence, by preserving the few high-magnitude transform coefficients that convey mostly the true-signal energy and discarding the rest, which are mainly due to noise, the true signal can be effectively estimated. The sparsity of the representation depends on both the  transform and the true-signal’s properties.

output code :

peaksnr before = 23.659551097946633

snr before = 13.786858445526661

peaksnr after = 27.636168606872697

snr after = 17.763475954452726

ssimValues before = 0.221353701319673

ssimValues after = 0.459691527385873

Input image :

01 input 1 image noise filtering

Noisy image :

MATLAB code download wavelet denoised image noise filtering

output image :

MATLAB code output MRI medical image

references :

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Local features in image processing

 

Support Vector Machines

 

Support Vector Machine

 

 

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