Using anisotropic diffusion equations in pixon domain for image de-noising

Nadernejad, Ehsan; Sharifzadeh, Sara; Forchhammer, Søren

doi:10.1007/s11760-012-0356-7

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…In recent years, a large number of complex denoising algorithms mean of nonlinear filter has appeared. Common algorithms include a variety of adaptive median filter algorithms: the wavelet threshold [4][5][6][7][8] (also called wavelet shrinkage) algorithm, the anisotropic diffusion equation algorithm [9][10][11][12], the total variation minimization algorithm [13][14][15][16], non-local mean filter algorithm [17][18][19][20], etc.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm

Liu¹

2015

IJSIP

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In the process of image acquisition and transmission, noise is always contained inevitably. So it is necessary to image denoising processing to improve the quality of image. Generally speaking, each algorithm has some filtering and threshold parameters. Taking variety kinds of images into account, it is a key problem of how to set these parameters in denoising algorithms under different conditions to achieve better performance. There are many algorithms for the determination of the parameters, and each of them has its application field. Because the wavelet transform has good performance, therefore, it has been widely applied as a kind of signal and image processing tools. In this paper, wavelet transform is used in the image denoising, and the genetic algorithm is used to estimate the denoising results. Experimental results show the validity of the new algorithm.

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Section: Introductionmentioning

confidence: 99%

Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm

Liu¹

2015

IJSIP

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“…Unfortunately, these methods are not effective for preserving image edges and texture details. In recent years, novel image denoising methods based on the wavelet transform [1], the nonlocal mean [2], and the partial differential equation (PDE) [3][4][5][6] have been proposed to get clear and high-quality images. Thereinto, nonlinear anisotropic diffusion models based on the PDE are able to effectively relieve the contradiction between noise removal and edge preservation, which motivates the researchers' considerable interest.…”

Section: Introductionmentioning

confidence: 99%

Patch Similarity Modulus and Difference Curvature Based Fourth-Order Partial Differential Equation for Image Denoising

Bai

Zhang

Shangguan

et al. 2015

Mathematical Problems in Engineering

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The traditional fourth-order nonlinear diffusion denoising model suffers the isolated speckles and the loss of fine details in the processed image. For this reason, a new fourth-order partial differential equation based on the patch similarity modulus and the difference curvature is proposed for image denoising. First, based on the intensity similarity of neighbor pixels, this paper presents a new edge indicator called patch similarity modulus, which is strongly robust to noise. Furthermore, the difference curvature which can effectively distinguish between edges and noise is incorporated into the denoising algorithm to determine the diffusion process by adaptively adjusting the size of the diffusion coefficient. The experimental results show that the proposed algorithm can not only preserve edges and texture details, but also avoid isolated speckles and staircase effect while filtering out noise. And the proposed algorithm has a better performance for the images with abundant details. Additionally, the subjective visual quality and objective evaluation index of the denoised image obtained by the proposed algorithm are higher than the ones from the related methods.

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“…[11] Perona-Malik model has been considered as a useful tool for image noise removal and other areas of image processing. [121314] However, if we decide to restore noisy images using some methods starting from an input image, which led to a set of possible filter solutions by gradually removing noise, the crucial question is when to stop filtering in order to get the optimal restoration result. The objective is quite challenging, because the stopping time has a great effect on the output result.…”

Section: Introductionmentioning

confidence: 99%

An optimal partial differential equations-based stopping criterion for medical image Denoising

2014

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Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

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Using anisotropic diffusion equations in pixon domain for image de-noising

Cited by 10 publications

References 32 publications

Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm

Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm

Patch Similarity Modulus and Difference Curvature Based Fourth-Order Partial Differential Equation for Image Denoising

An optimal partial differential equations-based stopping criterion for medical image Denoising

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