Adaptive image denoising method based on fitting diffusion

Home > Archive>Volume 34, Issue 2, 2020 >97-106

Adaptive image denoising method based on fitting diffusion
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                        Zhou XianchunZhou Xianchun
1.School of Electronic and Information Engineering， Nanjing University of Information Science and Technology,2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology， Nanjing University of Information Science and Technology
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Zhang HaoyuZhang Haoyu
1.School of Electronic and Information Engineering， Nanjing University of Information Science and Technology,2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology， Nanjing University of Information Science and Technology
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Wu TingWu Ting
1.School of Electronic and Information Engineering， Nanjing University of Information Science and Technology,2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology， Nanjing University of Information Science and Technology
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Xu XinjuXu Xinju
1.School of Electronic and Information Engineering， Nanjing University of Information Science and Technology,2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology， Nanjing University of Information Science and Technology
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Zhai JinyuZhai Jinyu
1.School of Electronic and Information Engineering， Nanjing University of Information Science and Technology,2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology， Nanjing University of Information Science and Technology
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Clc Number:TP751；TN911.7-34
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Abstract:

This paper put forward an adaptive threshold image denoising algorithm based on fitting diffusion to deal with the problem of texture loss and edge degradation. The algorithm will first improve the diffusion coefficient of the diffusion equation, establish fitting diffusion coefficient, avoid filtering incomplete due to the rapid convergence and the problem of image excessive smoothing. Then, the threshold will be designed and improved, and it will be automatically controlled by the maximal image gray value and iterative times, which can keep the image edge and detail features. Last, the designed algorithm will be simulated. The experimental result shows that the proposed algorithm can enhance the performance of denoising and protection of edge and detail information of texture, the peak Signal to Noise Ratio is promoted drastically. The new algorithm has excellent performance and is beneficial to practical application.

Key words:fitting diffusion coefficient; anisotropy; P-M model; partial differential equation; adaptive threshold; image de-noising

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Online: June 15,2023
Published: January 31,2020

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