Kernel based telegraph-diffusion equation for image noise removal
type: Item
instance of: scholarly article
title: Kernel based telegraph-diffusion equation for image noise removal
zbMATH Open document ID: 1407.94030
DOI: 10.1155/2014/283751
author: Yu-Qian Yang
author: Cheng-yi Zhang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: The second-order partial differential equations have good performances on noise smoothing and edge preservation. However, for low signal-to-noise ratio (SNR) images, the discrimination between edges and noise is a challenging problem. In this paper, the authors propose a kernel based telegraph-diffusion equation (KTDE) for noise removal. In this method, a kernelized gradient operator is introduced in the second-order telegraph-diffusion equation (TDE), which leads to more effective noise removal capability. Experiment results show that this method outperforms several anisotropic diffusion methods and the TDE method for noise removal and edge preservation.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 35Q94
zbMATH DE Number: 7016070
Wikidata QID: Q59065420
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/283751
OpenAlex ID: W2028921745
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description: scientific article; zbMATH DE number 7016070