Research of Image Denoising Method with Immune Mechanism

LIU Huan; OUYANG Chun-juan; XIAO Gen-fu

Chinese

您当前的位置：

首页 >

文章列表页 >

Research of Image Denoising Method with Immune Mechanism

更新时间：2026-09-04

- Research of Image Denoising Method with Immune Mechanism
- Vol. 33, Issue 3, Pages: 77-80(2016)
- 作者机构：
  
  1. 井冈山大学机构电子与信息工程学院,江西,吉安,343009
  2. 井冈山大学流域生态与地理环境监测国家测绘地理信息局重点实验室,江西,吉安,343009
  3. 井冈山大学机电学院,江西,吉安,343009
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
移动端阅览
刘欢, 欧阳春娟, 肖根福. 免疫机理的图像去噪方法研究[J]. 微电子学与计算机, 2016, 33(3): 77-80.

LIU Huan, OUYANG Chun-juan, XIAO Gen-fu. Research of Image Denoising Method with Immune Mechanism[J]. 2016, 33(3): 77-80.
刘欢, 欧阳春娟, 肖根福. 免疫机理的图像去噪方法研究[J]. 微电子学与计算机, 2016, 33(3): 77-80. DOI：

LIU Huan, OUYANG Chun-juan, XIAO Gen-fu. Research of Image Denoising Method with Immune Mechanism[J]. 2016, 33(3): 77-80. DOI：

摘要

为了有效降低图像中的噪声

提出一种基于免疫机理的图像去噪方法.该方法通过模拟生物免疫系统中抗体、抗原间信息处理机制来去除图像中的噪声.一方面考虑中心像素与其一定邻域范围内像素间的抑制作用; 另一方面引入中心像素与邻域外其他像素间存在的相互刺激作用; 将两者结合加权决定该像素灰度估计值

获得去噪后的图像.对比实验结果表明: 与其他方法相比

此方法获得更高的峰值信噪比(PSNR)和等效视数(ENL)

具有更好的视觉效果

能更好地去除图像中的噪声并保留图像细节信息.该方法为图像去噪提供了一条新的途径

对今后的研究具有重要的参考价值.

Abstract

Aiming at denoising image more effectively

an image denoising method based on immune mechanism is proposed. It removes the noise by means of simulating the information processing mechanism correlated to the antibody and antigen in biological immune system. On the one hand

the inhibition between the central pixel and its neighborhood pixels within a certain range is considered. On the other hand

involving the simulation interconnected the central pixel with the others outside the neighborhood range

and then in virtue of the combination of the above two aspects and the weighted calculation as well

a nice denoised image can be obtained by means of estimating the pixel gray. Compared with other methods

the contrast experiment results indicate that the proposed approach can get higher PSNR and ENL with better visual quality

and reduce noise more effectively as well as preserve image detail information very well. It provides a new way and gives an important reference value for future research of image denoising.

关键词

Keywords

references

Li Haifang, Wang Li. Research of image clustering algorithm based on artificial immune[J]. Computer Engineering and Application, 2007, 43(20): 181-183. DOI: 181

Jing Wei. New image denoising model based on fractional-order partial differential equation[J]. Journal of Computer Application, 2011, 31(3): 753-756. DOI: 753

Zhong Y F, Zhang L P, Huang B, et al. An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery[J]. IEEE Transaction on Geoscience and Remote Sensing, 2006, 44(2): 420-431. DOI: 420

Zhang Yu, Wang Xiangyang. Nonlocal-means algorithm for image denoising based on wavelet moment in domain[J]. Journal of Chinese Computer Systems, 2012, 33(9): 2079-2082. DOI: 2079

Dong Y H, Li G M. A parallel PCG solver for MODFLOW[J]. Ground Water, 2009, 47(6): 845-850. DOI: 845

Xiong Bo, Yin Zhouping. Fast non localmeans for image de-noising on moving average and modified weight function[J]. Journal of Image and Graphics, 2012, 17(5): 628-634. DOI: 628

周志立, 万登沪. 基于曲波变换与整体变分的图像去噪算法[J]. 微电子学与计算机, 2013, 30(12): 61-63. DOI: 61

程东旭, 郑玉晖, 杨艳. 基于B样条小波的偏微分方程图像去噪方法[J]. 微电子学与计算机, 2012, 29(2): 184-187. DOI: 184

冯欣悦, 杨秋翔, 安雁艳, 等. 基于SVM和小波系数的图像去噪算法研究[J]. 微电子学与计算机, 2014, 31(8): 119-122. DOI: 119

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Implementation and verification of a dual-chain power gating strategy for low power design

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Monte carlo analysis of V-layout trench SiC neutron detector

Aerial small object detection algorithm based on improved RT-DETR

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰