计算机科学 ›› 2018, Vol. 45 ›› Issue (11): 278-282.doi: 10.11896/j.issn.1002-137X.2018.11.044

• 图形图像与模式识别 • 上一篇    下一篇

结合核函数与非线性偏微分方程的图像去噪方法

陈鹏, 张建伟   

  1. (南京信息工程大学数学与统计学院 南京210044)
  • 收稿日期:2017-10-11 发布日期:2019-02-25
  • 作者简介:陈 鹏(1989-),男,硕士生,主要研究方向为图像处理、模式识别、数值分析与算法,E-mail:987956275@qq.com;张建伟(1965-),男,博士,教授,博士生导师,主要研究方向为图像处理、模式识别、数值分析与算法,E-mail:15195915516@163.com(通信作者)。
  • 基金资助:
    本文受国家自然科学基金项目:空间约束的非对称多元混合模型图像富先验学习与反问题研究(61672293)资助。

Image Denoising Method Combining Kernel Function and Nonlinear Partial Differential Equation

CHEN Peng, ZHANG Jian-wei   

  1. (College of Mathematics and Statistics,Nanjing University of Information Science and Technology,Nanjing 210044,China)
  • Received:2017-10-11 Published:2019-02-25

摘要: 传统的非线性扩散滤波方法在图像去噪时使用传统的梯度算子,易导致细节缺失。针对该缺点,以非线性偏微分方程和图像结构信息为基础,构造一类带有自适应权值的非线性扩散滤波去噪方式。这种方式选用多方向的拉普拉斯算子模板,结合核函数,自适应地调节权重系数;利用非局部信息选取合适的搜索窗宽,以减小图像噪声的影响。实验证明,该方法既能较好地保存图像纹理细节,又可以达到很好的去噪效果。

关键词: 非局部信息, 非线性偏微分方程, 核函数, 拉普拉斯算子, 图像去噪

Abstract: The traditional nonlinear diffusion filtering methods use the traditional gradient operator in image denoising,which may easily lead to missing details.In view of this shortcoming,a denoising method based on nonlinear diffusion filter was constructed according to the nonlinear partial differential equation and the image structure information.In this method,the kernel function is used to adaptively adjust the weight coefficient in the multi direction Laplasse operator template,and the influence of image noise is weaken by selecting the appropriate search window width by using the nonlocal information.The experimental results show that this method can not only save the image texture details,but also achieve good denoising results.

Key words: Image denoising, Kernel function, Laplasse operator, Nonlinear partial differential equation, Non-local information

中图分类号: 

  • TP391.4
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