Image Restoration with Fuzzy Coefficient Driven Anisotropic Diffusion

Prasath, V. B. Surya; Delhibabu, Radhakrishnan

doi:10.1007/978-3-319-20294-5_13

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…3, and the objective reflection of the data in Table 1, we can see that the values of PSNR, SSIM and ENTROY of the denoising image with formula (15) are higher than that of formula (14). That implies that formula (15) is able to reflect the corresponding relationship better than formula (14) between the local variance of the image and the adaptive fractional order.…”

Section: Construction Of Adaptive Fractional Order Differential Operatormentioning

confidence: 90%

“…. Table 1 lists the comparison results of PSNR, SSIM and information entropy (ENTROY) between formula (14) and formula (15) as the adaptive operator, the variance of the Gaussian white noise is 15 δ = . From the subjective feelings of Fig.…”

Section: Construction Of Adaptive Fractional Order Differential Operatormentioning

confidence: 99%

“…G Ghimpeteanu, et al [13] considered an image decomposition model that provided a novel framework for image denoising. V.B.S Prasath, et al [14] proposed a fuzzy diffusion coefficient which takes into account local pixel variability for better denoising and selective smoothing of edges.…”

Section: Introductionmentioning

confidence: 99%

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Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

Yu¹,

Tan²,

Zhou³

et al. 2016

KSII TIIS

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Recently, the method based on fractional order partial differential equation has been used in image processing. Usually, the optional order of fractional differentiation is determined by a lot of experiments. In this paper, a denoising model is proposed based on adaptive fractional order anisotropic diffusion. In the proposed model, the complexity of the local image texture is reflected by the local variance, and the order of the fractional differentiation is determined adaptively. In the process of the adaptive fractional order model, the discrete Fourier transform is applied to compute the fractional order difference as well as the dynamic evolution process. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the proposed image denoising algorithm is better than that of other some algorithms. The proposed algorithm not only can keep the detailed image information and edge information, but also obtain a good visual effect.

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Section: Construction Of Adaptive Fractional Order Differential Operatormentioning

confidence: 90%

Section: Construction Of Adaptive Fractional Order Differential Operatormentioning

confidence: 99%

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Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

Yu¹,

Tan²,

Zhou³

et al. 2016

KSII TIIS

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show abstract

“…The typical disadvantages of the Perona-Malik model are that it can not effectively protect sharp edges and texture details during denoising, but this drawback can be overcame by using the appropriate diffusion coefficient. In order to better preserve detailed and texture information and greatly reduce the running time of the algorithm to improve the operational efficiency, according to [1,25], we proposed two new diffuse coefficients as (6) and (7). Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k; it does not need to do experiments to get k.…”

Section: The Proposed Methodmentioning

confidence: 99%

“…H. Zhang and Y. Zhang [6] proposed an adaptive diffusion coefficient scheme based on anisotropic diffusion. Prasath and Delhibabu [7] proposed a fuzzy diffusion coefficient which takes into account local pixel variability for better denoising and selective smoothing of edges. Sun et al [8] have investigated the edges and details blurring issues.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Adaptive Fractional Order with Improved PM Model

Zhai

Zhou

et al. 2018

Mathematical Problems in Engineering

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In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k, and it does not need to do any experiments to get the value of k. For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.

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A New Image Super-Resolution Reconstruction Algorithm Based on Hybrid Diffusion Model

Yin

Huang

et al. 2021

Image and Graphics Technologies and Applications

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Image Restoration with Fuzzy Coefficient Driven Anisotropic Diffusion

Cited by 6 publications

References 18 publications

Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

Image Denoising Based on Adaptive Fractional Order with Improved PM Model

A New Image Super-Resolution Reconstruction Algorithm Based on Hybrid Diffusion Model

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