Multiplicative Noise Removal for Texture Images Based on Adaptive Anisotropic Fractional Diffusion Equations

Yao, Wenjuan; Guo, Zhichang; Sun, Jinwei; Wu, Boying; Gao, Huijun

doi:10.1137/18m1187192

Cited by 43 publications

(20 citation statements)

References 55 publications

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“…Li and Xie applied the concept of fractional calculus of small probability strategy [16]. Other fractional approaches utilized by considering different types of diffusion equations, smooth diffusion equation, differential operators, integral operators of fractional order, fractional entropy and Wavelet energy entropy (see for recent efforts [17][18][19][20]). All these methods described fractional masks by using different values of gamma function and its compositions.…”

Section: Related Workmentioning

confidence: 99%

“…Image processing involves a number of phases, where denoising of images is the first responsibilities to be assumed and the recent challenge in this direction of studies is denois in images multiplicatively (DIM). DIM simulations are dominant to the study of logical imaging structures [1,2], by means of SAR ( synthetic aperture radar), laser imaging and ultrasound imaging. Due to the logical environment of these image achievement procedures, the normal additive noise structure is insufficient for meting out such images.…”

Section: Introductionmentioning

confidence: 99%

“…In its place, DIM simulations deliver a high score explanation of logical imaging structures. There are different techniques deal with removing DIM based on the diffusion equation formal, partial differential equations, variation methods, gradient operational method and fractional differential equations [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Ibrahim¹

2019

SN Appl. Sci.

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Reducing noise from images is an essential structure of the image processing study. Noises can arise with images through achievement on diffusion. The existence of noise can delay the right operation of these images for many applications such as satellite and medical images. Reducing denois in images multiplicatively (DIM) has been developed and modified by many researchers during the past few years. DIM can destroy almost all data of the original image, especially the texture of images. Our aim is to present a new technique to solve this problem. The technique is based on a new fractional calculus called the conformable fractional calculus (CFC). This type of calculus has advantages because of its formula involves a controller, which can be applied to complex problems such as DIM. The proposed structures of CFC windows are given by four masks suggested for x and y directions. On four directional angles, a convolution operational product of the input image pixels with a CFC mask window has been completed. The visual observation and peak signal-to-noise ratio with Root Mean Square Error are employed for measurements. The experiments showed that the skillful filtering outcomes are indicated high score than some well known filers such as Gaussian filter, Sobel edge filter, Canny edge filter and gray-level co-occurrence matrix. Compering is illustrated as well with newly researches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Ibrahim¹

2019

SN Appl. Sci.

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“…Many approaches have been considered for the multiplicative noise removal [Liu and Fan, 2016;Ullah et al, 2017;Zhao, Wang, and Ng, 2014;Dong et al, 2017]. Among of them, Total variation (TV) based approaches have achieved great success [Li, Wang, and Zhao, 2016;Li, Lou , and Zeng, 2016;Zhou et al, 2015;Yao et al, 2019;Bai, 2019;Aubert and Aujol, 2008;Dong and Zeng, 2013]. In [Aubert and Aujol, 2008], the authors proposed a multiplicative noise removal model as follows (M1 model):…”

Section: Introductionmentioning

confidence: 99%

A hybrid regularizers model for multiplicative noise removal

Tran

Pham

et al. 2021

CAP

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In this paper, we propose a variational method for restoring images corrupted by multiplicative noise. Computationally, we employ the alternating minimization method to solve our minimization problem. We also study the existence and uniqueness of the proposed problem. Finally, experimental results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for image denoising in comparison with state-of-the-art methods.

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“…This attraction is characterized by several modifications of the Perona-Malik model that are published in different journals (Guo et al, 2012;Kessy et al, 2017a;Kessy et al, 2017b;Maiseli et al, 2018). These works are establishing stable and accurate models that deal with different noise variants and staircase artifacts caused by the ill-posed aspect associated with the partial differentiation applied in the Perona-Malik kernel (Liu et al, 2013;Jain and Ray, 2019;Yao et al, 2019). In general, the Perona-Malik model is made of a diffusion kernel functional that approximates the pixel value and the regularization term, which has been added to control the illposed aspect of the model and prevents staircase artifacts in the despeckled image.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction

Christophe

Anthony

Kapyela

et al. 2020

TJET

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Multiplicative and additive noises are often introduced in image signals during the image acquisition process and result into degradation of image features. The work done by Perona and Malik in 1990 and its modified versions revolutionized the way through which noises or speckles are removed. The Perona-Malik model requires tuning of the regularization parameter to control and prevent staircase artifacts in restored images. The current manual tuning is a challenging and time consuming practice when a long queue of images is registered for processing. Attempt to automate the regularization parameter appeared in Perona-Malik model with self-adjusting shape-defining constant. Although both multiplicative and additive noise based automated regularizations were presented, the paper stayed silent on matters concerning the automation method that fits with speckle reduction. This paper therefore, presents a comparative analysis of additive and multiplicative noise based automated regularizations. Simulation results and paired samples T-tests reveal that the multiplicative noise based automation outperforms the additive noise based automation for small speckle variances. However, the two automation methods do not significantly differ when large speckle variances are assumed.

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Multiplicative Noise Removal for Texture Images Based on Adaptive Anisotropic Fractional Diffusion Equations

Cited by 43 publications

References 55 publications

A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

A hybrid regularizers model for multiplicative noise removal

Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction

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