Reaction–diffusion equation based image restoration

Zhao, Xueqing; Huang, Keke; Wang, Xiaoming; Shi, Meihong; Zhu, Xiao; Gao, Quanli; Yu, Zhaofei

doi:10.1016/j.amc.2018.06.054

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Wen et al [15] introduced a statistical texture analysis method based on texture element mode, which can provide spatial structure information of oil painting texture and has the monotonic invariance of pixel gray value. Zhao et al [16] proposed a texture analysis method based on BVLC moments and BDIP moments. BVLC can display rough and smooth characteristics, and BDIP can extract troughs and edges very well.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation

Huang¹

2021

Advances in Mathematical Physics

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The oil painting retrieval technology based on the reaction diffusion equation has attracted widespread attention in the fields of oil painting processing and pattern recognition. The description and extraction of oil painting information and the classification method of oil paintings are two important processes in content-based oil painting retrieval. Inspired by the restoration and decomposition functional model of equal oil painting, we propose a reaction diffusion equation model. The new model contains two reaction diffusion equations with different principal parts. One principal part is total variation diffusion, which is used to remove noise. The other main part is thermal diffusion, which is used to modify the source term of the denoising reaction-diffusion equation to achieve the effect of protecting the texture of the oil painting. The interaction of the two reaction-diffusion equations finally achieves denoising while maintaining the boundaries and textures. Under the framework of the above reaction diffusion equation model, we introduce Laplace flow to replace the original total variation flow, so that the new denoising reaction diffusion equation combines the isotropic diffusion and total variation flow of the thermal reaction diffusion equation to achieve the effect of adaptive theoretical research. Using regularization methods and methods, we, respectively, get the well-posedness of the two model solutions, which provides the necessary preparation for numerical calculations. Based on the statistical theory and classification principles of support vector machines, combined with the characteristics of oil painting classification, the research and analysis are carried out from the three important aspects of kernel function, training algorithm, and multiclass classifier algorithm that affect the classification effect and speed. Numerical experiments show that the given filter model has a better processing effect on images with different types and different degrees of noise pollution. On this basis, an oil painting classification system based on texture features is designed, combined with an improved gray-level cooccurrence matrix algorithm and a multiclass support vector machine classification model, to extract, train, and classify oil paintings. Experiments with three types of oil paintings prove that the system can achieve a good oil painting classification effect. Different from the original model, the new model is based on the framework of reaction-diffusion equations. In addition, the new model has good effects in removing step effects, maintaining boundaries and denoising, especially in maintaining texture.

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Section: Related Workmentioning

confidence: 99%

Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation

Huang¹

2021

Advances in Mathematical Physics

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“…RD dynamics have been used to describe a large variety of physical systems, e.g., epidemiology, as recently applied to the spread of COVID-19 (Mammeri, 2020), pattern formation in biology (Kondo and Miura, 2010), demographics and paleoanthropology (where human presence and expansion has been modelled through RD equations, accounting for environmental factors (Steele et al, 1998)), linguistics (as in the case of language death processes (Abrams and Strogatz, 2003)), population dynamics (Volpert and Petrovskii, 2009), ecology (Cosner, 2008), finance (Mastromatteo et al, 2014), digital image restoration (Zhao et al, 2018) and particle physics (Toussaint and Wilczek, 1983).…”

Section: Introductionmentioning

confidence: 99%

Effects of gravity modulation on the dynamics of a radial A+B→C reaction front

Stergiou

Hauser

Comolli

et al. 2022

Chemical Engineering Science

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“…During the image acquisition, signal amplification and transmission, which are often corrupted by different types of noise, the most noise is Additive Gaussian noise and impulse noise [1][2][3][4] , both of them have significantly influenced the image processing. Filtering noise and preserving the image features from the corrupted images are important parts of image pre-treatments.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonlinear Filter using Main Texture Direction for Mixed Noise in Color Image Processing

Zhao¹,

Shi²,

Shi³

et al. 2017

dtcse

Self Cite

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Adaptive nonlinear filter (ANF) using main texture direction is proposed for the removal of mixed noise from corrupted color images processing. The purpose of this work is to focus on designing noise detector and noise filter. For the detector, Chebyshev's theorem and the fuzzy mean process are used to estimate adaptive parameters of detector; for the filter, the authors use the local texture direction probability density distributions which are gained by performing the Radon transform of the image. Extensive experimental results show that the proposed ANF outperforms other filters in terms of important evaluation metrics; in particular, the computational complexity of the ANF is much lower than the test filters.

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Reaction–diffusion equation based image restoration

Cited by 7 publications

References 20 publications

Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation

Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation

Effects of gravity modulation on the dynamics of a radial A+B→C reaction front

Adaptive Nonlinear Filter using Main Texture Direction for Mixed Noise in Color Image Processing

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