Perona–Malik model with self-adjusting shape-defining constant

Maiseli, Baraka; Msuya, Hubert; Kessy, Suzan; Kisangiri, Michael

doi:10.1016/j.ipl.2018.04.016

Cited by 9 publications

(11 citation statements)

References 35 publications

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“…Several experimental results show that this integration strategy gives a balanced interplay between total variation and Perona–Malik models, a consequence that signals generation of high quality and informative scenes. In addition, the integration process establishes a hybrid regularisation model that masks the weaknesses of total variation and Perona–Malik models, which suffer from contrast losses, blocky artefacts, and stair‐casing effects [39, 40].…”

Section: Augmenting Pvr Systemsmentioning

confidence: 99%

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

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Section: Augmenting Pvr Systemsmentioning

confidence: 99%

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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“…The Perona-Malik model significantly attracted scholars' attention due to its edge preservation capabilities. 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).…”

Section: Introductionmentioning

confidence: 99%

“…The classical regularization requires manual tuning of the Lagrange multiplier or regularization parameter. This manual tuning consumes time and is harmful in domains that operate under high workload and high-level of accuracy (Maiseli et al, 2018). The regularization parameter should be correctly chosen so that a proper image can be recovered (Liu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The regularization parameter should be correctly chosen so that a proper image can be recovered (Liu et al, 2013). The study that automates the regularization parameter is presented in Perona-Malik model with self-adjusting shape-defining constant whereby automation analysis based on both additive and multiplicative noise models came up with two distinct automated regularization parameter formulae (Maiseli et al, 2018). However, authors did not present a formulation that fits with speckle granularities and proprieties.…”

Section: Introductionmentioning

confidence: 99%

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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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“…This method was further developed in many works, e.g. [2][3][4]. The idea of this method is relatively simple: authors suggest to numerically solve PDE (for instance stationary diffusion equation) with image being denoised as initial conditions.…”

Section: Introductionmentioning

confidence: 99%

A Fully Conservative Parallel Numerical Algorithm with Adaptive Spatial Grid for Solving Nonlinear Diffusion Equations in Image Processing

Bulygin

Vrazhnov

2019

JSFI

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In this paper we present simple yet efficient parallel program implementation of grid-difference method for solving nonlinear parabolic equations, which satisfies both fully conservative property and second order of approximation on non-uniform spatial grid according to geometrical sanity of a task. The proposed algorithm was tested on Perona-Malik method for image noise filtering task based on differential equations. Also in this work we propose generalization of the Perona-Malik equation, which is a one of diffusion in complex-valued region type. This corresponds to the conversion to such types of nonlinear equations like Leontovich-Fock equation with a dependent on the gradient field according to the nonlinear law coefficient of diffraction. This is a special case of generalization of the Perona-Malik equation to the multicomponent case. This approach makes noise removal process more flexible by increasing its capabilities, which allows achieving better results for the task of image denoising.

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Perona–Malik model with self-adjusting shape-defining constant

Cited by 9 publications

References 35 publications

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

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

A Fully Conservative Parallel Numerical Algorithm with Adaptive Spatial Grid for Solving Nonlinear Diffusion Equations in Image Processing

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