A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

Jain, Subit K.; Ray, Rajendra K.; Bhavsar, Arnav

doi:10.1007/s00034-018-0913-6

Cited by 13 publications

(7 citation statements)

References 45 publications

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“…The literature [11] proposed an edge enhancement algorithm with the help of fuzzy theory to obtain the image feature values, and the method is good for enhancing the image detail information. The literature [12] proposed a fuzzy contrast enhancement algorithm that adapts the local contrast for image enhancement. The literature [13] proposes an algorithm that first selects weights with the help of fuzzy rules to minimize the mean squared error of the image output, and experiments show that this rule-controlled median filter enhancement algorithm is 2…”

Section: Related Studiesmentioning

confidence: 99%

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Chen

2021

Advances in Mathematical Physics

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In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

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Section: Related Studiesmentioning

confidence: 99%

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Chen

2021

Advances in Mathematical Physics

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“…Furthermore, the above study outcomes stated the effectiveness of the proposed filter as compared to HSA or other filtering mechanisms. Subit and et al [16] have introduced different equations of partial different for handling function of diffusion and the term fidelity as well. An energy functional was derived to study the associated evolution issues by utilising an approach of posterior regularization corresponding to the denoised image for recovery.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Now each hidden neuron sums the weight of the input pixels ) and the output pixel is calculated using the following derivation; (14) (15) Where, is the input neuron, y is the output neuron, is the sum of weighted input pixel, is the input weight, is the initial weight. Now, the error is calculated by comparing the output and the target which is defined as (16) The output is used for correction of weight first and to update the weight at the end is represented as (17) (18) Now to update the weight of the pixels to obtain the output neuron y is calculated using the following functions 19The process will stop until the error measure (MSE) is less than the target error or the epoch/iteration less than the maximum epoch/iterations. The value of iteration is set as 1000.…”

Section: Figure 3: Sample Of 3*3 Median Filter Modelmentioning

confidence: 99%

A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

Uplaonkar*¹,

Amarapur²

2019

IJRTE

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Pre-processing of image is considered to be an important aspect medical image analysis in order to enhance its quality. Noise reduction is performed for improvement in the visual quality and removing redundant image values. Ways of pre-processing of image are a necessity, for removing the noise and for quality enhancement of the image. Before applying any approach on medical images, measures used to pre-process seem to be vital for limiting the abnormalities’ findings with no influence from background of the medical image. In this work, a filter is proposed based on Adaptive Median and Weiner Hybrid Neural Filter for noise reduction. The review of filtering techniques is implemented using MATLAB platform, followed by comparison of results with different filtering techniques to show the system effectiveness.

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“…In the above relation, J is the degraded image, I the clean image, and η the speckle noise follows the Gamma(L, L) distribution [33], where L is a positive integer denotes the number of "looks" [5,26,39].…”

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confidence: 99%

“…Among the popular state-ofthe-art despeckling approaches, partial differential equation (PDE) based methods are widely used to formulate the speckle noise removal strategies. Well-known PDE based approaches are nonlinear diffusion-based methods [33,34,50,58,61,62] and variational based methods [7,19,29,35,40,49,51,54]. In general, the diffusion processes in image processing have their origin in the variational calculus [8].…”

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confidence: 99%

A fuzzy edge detector driven telegraph total variation model for image despeckling

Majee¹,

Jain²,

Ray³

et al. 2022

IPI

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<p style='text-indent:20px;'>Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.</p>

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A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

Cited by 13 publications

References 45 publications

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

A fuzzy edge detector driven telegraph total variation model for image despeckling

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A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

Cited by 13 publications

References 45 publications

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

A fuzzy edge detector driven telegraph total variation model for image despeckling

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Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection