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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 5
1573-1576

Dual kernel non-local means denoising algorithm based on gradient feature

Zhang Yuzheng1,2
Yang Cihui1,2
Lin Quan1
1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
2. Key Laboratory of Jiangxi Province for Image Processing & Pattern Recognition, Nanchang 330063, China
DOI: 10.19734/j.issn.1001-3695.2017.12.0830

Abstract

In the traditional non-local mean(NLM) filtering algorithm, the presence of noise in the image interferes the accuracy of similarity calculation between neighborhood blocks. To address this problem, this paper proposed a dual kernel non-local means denoising algorithm based on gradient feature. This algorithm calculated the similarity of neighborhood block by Euclidean distance and gradient feature between pixels, and replaced the original exponential function with dual kernel function to calculate similar weights. In addition, it measured the similarity between the neighborhood blocks of the pixels in the search area and the current neighborhood to reassign the pixels' weight. On this basis, it reevaluated the denoising values and got the denoised image. Experimental results show that this algorithm can accurately reflect the similarity between neighborhood block of pixels and preserve the details and edge information of images effectively while compared with the traditional NLM filtering algorithm and two other improved NLM algorithms, and improves the effect of image denoising significantly.

Foundation Support

国家青年自然科学基金资助项目(61402218)
江西省图像处理与模式识别重点实验室开放基金资助项目(TX201304002)
南昌航空大学研究生创新基金资助项目(YC2016043)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0830
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 5
Section: Technology of Graphic & Image
Pages: 1573-1576
Serial Number: 1001-3695(2019)05-061-1573-04

Publish History

[2019-05-05] Printed Article

Cite This Article

张玉征, 杨词慧, 林泉. 基于梯度特征的双核非局部均值去噪算法 [J]. 计算机应用研究, 2019, 36 (5): 1573-1576. (Zhang Yuzheng, Yang Cihui, Lin Quan. Dual kernel non-local means denoising algorithm based on gradient feature [J]. Application Research of Computers, 2019, 36 (5): 1573-1576. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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