[1]张化朋,任少美. 基于偏微分方程的乘性噪声去噪算法[J].计算机技术与发展,2016,26(11):90-92.
　ZHANG Hua-peng,REN Shao-mei. Algorithm of Removing Multiplicative Noise Based on Partial Differential Equation[J].,2016,26(11):90-92.
点击复制

 基于偏微分方程的乘性噪声去噪算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年11期
页码:
90-92
栏目:
智能、算法、系统工程
出版日期:
2016-11-10

文章信息/Info

Title:
 Algorithm of Removing Multiplicative Noise Based on Partial Differential Equation
文章编号:
1673-629X（2016)11-0090-03
作者:
 张化朋; 任少美
 南京邮电大学 理学院
Author(s):
 ZHANG Hua-peng; REN Shao-mei
关键词:
 ; 图像去噪; 合成孔径雷达图像; 伽马噪声; 偏微分方程; 交替迭代法
Keywords:
 ; image denoising; synthetic aperture radar image; Gamma noise; Partial Differential Equation ( PDE) ; alternating minimization algorithm
分类号:
TP301.6
文献标志码:
A
摘要:
 图像去噪是图像处理中最基本的问题，也是当前研究的热点。近年来，国内外学者对去除乘性噪声进行了大量的研究，在AA模型的基础上提出了许多去除合成孔径雷达图像中的伽马噪声的模型，它们都可以有效地去除图像中的噪声，但是共同的缺点是原图像的细节丢失并且计算速度慢。针对这些问题，引入了权重函数，在此基础上给出一种基于偏微分方程的去除图像乘性噪声的变分模型。然后使用交替迭代法，求出了该模型的数值解，并从理论上说明了该迭代序列的收敛性。数值实验结果表明，所提出的模型保持了较好的去噪效果，能够较好地抑制图像中的“阶梯效应”；与其他模型相比，该算法处理速度快，极大地缩短了运算时间。
Abstract:
 Image denoising is the most basic problem in image processing and also the current research focus. In recently years,there are a lot of researches on the multiplicative noise removal by domestic and foreign scholars. Based on AA model,many models for removal of Gamma noise in synthetic aperture radar image are proposed,which can remove the noise effectively,but the common disadvantage is loss of the original image’ s detail and slow computing speed. Aiming at them,introducing weight function,a new variational model based on partial differential equation is proposed to remove the multiplicative noise. An alternating minimization algorithm is introduced to solve the problem for the model. What’ s more,the convergence for the variational problem is illustrated in theroy. Experimental results show that the proposed model has a good denoising effect and restrains the“staircase effect”. Compared with the other models,the algorithm is faster and greatly decreases the computational time.

相似文献/References:

[1]黄伟 王书文.一种基于图像边缘检测的全变分的去噪方法[J].计算机技术与发展,2009,(02):24.
　HUANG Wei,WANG Shu-wen.Total Variation Image Denoising Based on Image Edge Detection[J].,2009,(11):24.
[2]周先国 李开宇.基于提升小波结合DCT变换的图像去噪研究[J].计算机技术与发展,2009,(02):62.
　ZHOU Xian-guo,LI Kai-yu.Image De - noising Research Based on Lifting Wavelet and Discrete Cosine Transform[J].,2009,(11):62.
[3]邢丹俊 王继成.基于提升小波的自适应阈值图像去噪[J].计算机技术与发展,2008,(02):42.
　XING Dan-jun,WPNG Ji-cheng.Adaptive Threshold Based on Lifting Wavelet Transform for Image Denoising[J].,2008,(11):42.
[4]于梅 殷兵 梁栋 王璐.基于边缘检测的Contourlet变换图像去噪[J].计算机技术与发展,2008,(06):104.
　YU Mei,YIN Bing,LIANG Dong,et al.Image Denoising with Contourlet Transform Based on Edge Detection[J].,2008,(11):104.
[5]林德贵 何建农 郑玉燕.基于分水岭的提升小波图像去噪[J].计算机技术与发展,2008,(08):29.
　LIN De-gui,HE Jian-nong,ZHENG Yu-yan.Lifting Wavelet Image De- Noising Based on Watershed[J].,2008,(11):29.
[6]汪继文 林胜华 沈玉峰 邱剑锋.一种基于各向异性扩散的图像处理方法[J].计算机技术与发展,2008,(08):98.
　WANG Ji-wen,LIN Sheng-hua,SHEN Yu-feng,et al.An Approach for Image Restoration Based on Anisotropic Diffusion[J].,2008,(11):98.
[7]李瑶 董瑞 何韬 梁栋.一种基于Contourlet变换的图像去噪方法[J].计算机技术与发展,2007,(03):81.
　LI Yao,DONG Rui,HE Tao,et al.A Method for Image Denoising Based on Contourlet Transform[J].,2007,(11):81.
[8]王国权 刘亮.FoE模型的训练方法研究[J].计算机技术与发展,2010,(12):86.
　WANG Guo-quan,LIU Liang.A Study on Training Methods of FoE Model[J].,2010,(11):86.
[9]方洁.基于非抽样Contourlet变换的最佳软阈值图像去噪[J].计算机技术与发展,2011,(02):102.
　FANG Jie.Image Denoising Based on Nonsubsampled Contourlet Transform and Best Soft Threshold[J].,2011,(11):102.
[10]柯丹丹 蔡光程 曹倩倩.基于形态学算子的各向异性扩散去噪方法[J].计算机技术与发展,2012,(04):81.
　KE Dan-dan,CAI Guang-cheng,CAO Qian-qian.An Anisotropic Diffusion Denoising Method Based on Morphological Operator[J].,2012,(11):81.
[11]李智,张根耀,王蓓. 基于一种新的阈值函数的小波图像去噪[J].计算机技术与发展,2014,24(11):100.
　LI Zh,ZHANG Gen-yao,WANG Bei. Wavelet Image Denoising Based on a New Threshold Function[J].,2014,24(11):100.
[12]张哲,张化朋. 一种基于偏微分方程变分去噪模型[J].计算机技术与发展,2014,24(11):103.
　ZHANG Zhe,ZHANG Hua-peng. An Denoising Model of Variation Based on PDE[J].,2014,24(11):103.
[13]王璐. 基于四阶微分全变差的图像去噪模型[J].计算机技术与发展,2016,26(03):85.
　WANG Lu. Image De-noising Model Based on Total Variation of Fourth-order Differential[J].,2016,26(11):85.
[14]李晓飞,邱晓晖. 基于小波变换的改进软阈值图像去噪算法[J].计算机技术与发展,2016,26(05):76.
　LI Xiao-fei,QIU Xiao-hui. An Improved Soft-threshold Image Denoising Algorithm Based on Wavelet Transform[J].,2016,26(11):76.
[15]黄玲俐. 一种改进权重的非局部均值图像去噪方法[J].计算机技术与发展,2016,26(06):16.
　HUANG Ling-li. A Non-local Means Denoising Algorithm with Improved Weighted Function[J].,2016,26(11):16.
[16]张小燕,吐尔洪江·阿布都克力木. 小波变换的阈值图像去噪算法改进[J].计算机技术与发展,2017,27(03):81.
　ZHANG Xiao-yan,Turghunjan ABDUKIRIM TURKI. Improvement of Threshold Image Denoising Algorithm with Wavelet Transform[J].,2017,27(11):81.
[17]陈少利,杨敏. 改进变步长快速迭代收缩阈值算法[J].计算机技术与发展,2017,27(10):69.
　CHEN Shao-li,YANG Min. An Improved Fast Iterative Shrinkage-thresholding Algorithm with Variable Stepsize[J].,2017,27(11):69.
[18]雷飞,朱林,王雪丽. 改进小波软硬折衷法在水下图像去噪中的应用[J].计算机技术与发展,2017,27(11):150.
　LEI Fei,ZHU Lin,WANG Xue-li. Application of Improved Compromise Soft-hard Threshold Algorithm in Underwater Image Denoising[J].,2017,27(11):150.

更新日期/Last Update: 2016-12-13