An advanced and adaptive mathematical function for an efficient anisotropic image filtering

Tebini, Sondes; Seddik, Hassene; Braiek, Ezzedine Ben

doi:10.1016/j.camwa.2016.07.004

Cited by 28 publications

(15 citation statements)

References 32 publications

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“…As a result of the stochastic nature of the noise, the results vary according to a method. In 18 FODF, 20 HDC, 16 AADF, 19 and ADC, 17 the proposed method achieves the highest PSNR, SSIM, and best MSE in all cases.…”

Section: Quantitative Analysis Comparison With Pde-based Diffusivity ...mentioning

confidence: 93%

Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Kollem¹,

Reddy²,

Rao

et al. 2021

Int J Imaging Syst Tech

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Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function-based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross-validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth-order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal-to-noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods.

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Section: Quantitative Analysis Comparison With Pde-based Diffusivity ...mentioning

confidence: 93%

Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Kollem¹,

Reddy²,

Rao

et al. 2021

Int J Imaging Syst Tech

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“…Y K model proposed the following edge‐stopping function:

c ()(, (||, \nabla^{2} u)) = \frac{1}{1 + {()(, (||, \nabla^{2} u) / k)}^{2}}

where

\nabla^{2} = normalΔ

denotes Laplacian operator. Mohammad [21] proposed the following edge‐stopping function:

c ()(, (||, \nabla u)) = \frac{1}{1 + {()(, (||, \nabla u) / k)}^{2}}

The edge indicator mentioned above is either based on Laplacian operator or gradient operator, both may not effectively detect edges [15]. Obviously, the isotropic Laplacian operator can better identify the weak edges or textures, but it is sensitive to the noise and tends to generate multiple false edges.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The staircase effect is an inherent property of the second‐order partial differential equation (PDE), and it mainly comes from the evolution from an observed image towards piecewise‐level image. An effective way to solve this drawback is increasing the order of derivatives in the diffusion model and the fourth‐order PDEs have been of special interest in recent years [15]. The earliest fourth‐order YK is known to overcome staircases.…”

Section: Related Workmentioning

confidence: 99%

Image smoothing via adaptive fourth‐order partial differential equation model

Wang

Chen

Yao

et al. 2019

J. eng.

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“…This technique overcomes the blurring edges and lines of an image and improves the ability to suppress noise when the noise level is high. SondesTebini et al [20], Anisotropic Diffusion (AD) is used to suppress the noise from an image with unvarying the important basic structural properties of an original image. Image blurring is a major disadvantage in AD.…”

Section: Fig 6 Calculating the Median Value Of A Pixel Neighborhood mentioning

confidence: 99%

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

2019

IJITEE

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Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metrics.

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An advanced and adaptive mathematical function for an efficient anisotropic image filtering

Cited by 28 publications

References 32 publications

Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function

Image smoothing via adaptive fourth‐order partial differential equation model

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

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