An improved anisotropic diffusion algorithm for the Research of Image Denoising

Home > Archive>Volume 45, Issue 17, 2022 >113-119

An improved anisotropic diffusion algorithm for the Research of Image Denoising
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                        Zhang XinruZhang Xinru
1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
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Zhou Xianchun3Zhou Xianchun
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Wang Zhifei1Wang Zhifei
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Wang Wenyan3Wang Wenyan
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Yang Chuanbing3Yang Chuanbing
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Affiliation:1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China; 3. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Clc Number:TP751.1;TN911.73
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Abstract:

In order to solve the problem of texture loss and image edge degradation, an improved anisotropic diffusion model is proposed in this paper. Firstly, the PM model and LCC model are combined. According to the changes of image gradient, the relationship between the gradient modulus of local image and the diffusion intensity is constructed. Different gradient modulus values are selected for different diffusion functions. Then, the -norm is used to determine the gradient threshold in the diffusion function, which further improves the generalization ability of the proposed model. Experimental results show that this model can not only solve the problem of outliers existing in the traditional PM model, but also effectively protect the integrity of image edge features and contour structure. Compared with the original algorithm, the image signal-to-noise ratio is improved by 1.47~1.57dB, and the structural similarity is improved by 17%, the denoising efficiency is improved while ensuring the denoising effect.

Key words:anisotropic diffusion; PM model; diffusion function; -norm

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Online: April 02,2024
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