Image denoising based on adaptive fractional order with improved PM model
type: Item
instance of: scholarly article
title: Image denoising based on adaptive fractional order with improved PM model
zbMATH Open document ID: 1427.94031
DOI: 10.1155/2018/9620754
author: Jimin Yu
author: Rumeng Zhai
author: Shangbo Zhou
author: Lijian Tan
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter \textit{k}, and it does not need to do any experiments to get the value of \(k\). For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 35K59
zbMATH DE Number: 7019799
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/9620754
OpenAlex ID: W2803807298
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Wikidata QID: Q129804798
description: scientific article; zbMATH DE number 7019799