[1]李俊泽,袁小芳,张振军,等.一种基于二维GARCH模型的图像去噪方法[J].智能系统学报,2015,10(01):62-67.[doi:10.3969/j.issn.1673-4785.201403066]
 LI Junze,YUAN Xiaofang,ZHANG Zhenjun,et al.A method of image denoising based on two-dimensional GARCH model[J].CAAI Transactions on Intelligent Systems,2015,10(01):62-67.[doi:10.3969/j.issn.1673-4785.201403066]
点击复制

一种基于二维GARCH模型的图像去噪方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
62-67
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
A method of image denoising based on two-dimensional GARCH model
作者:
李俊泽1 袁小芳1 张振军1 王耀南1 王国锋2
1. 湖南大学 电气与信息工程学院, 湖南 长沙 410082;
2. 中国公路工程咨询集团有限公司, 北京 100097
Author(s):
LI Junze1 YUAN Xiaofang1 ZHANG Zhenjun1 WANG Yaonan1 WANG Guofeng2
1. School of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
2. China Highway Engineering Consulting Corporation, Beijing 100097, China
关键词:
小波变换统计建模二维GARCH模型果蝇优化算法图像去噪
Keywords:
wavelet transformstatistical modelingtwo-dimensional GARCH modelFOAimage denoising
分类号:
TP751.1
DOI:
10.3969/j.issn.1673-4785.201403066
文献标志码:
A
摘要:
提出了一种基于小波系数统计模型的图像去噪方法。该方法利用二维广义自回归条件异方差(2D-GARCH)模型对小波系数进行建模,这种小波系数模型能够更好地利用小波系数“尖峰厚尾”的分布特性和系数间的相关性等重要特性。利用基于果蝇优化算法的极大似然估计(ML Estimation based on FOA)代替传统的线性规划方法求解模型参数,提高了建模的准确性。在此基础上再采用最小均方误差估计(MMSE Estimation)对未受噪声污染的原始图像的小波系数进行估计。实验结果表明,与当前主流的去噪方法相比,该算法能更有效地去除图像中的噪声,获得更高的峰值信噪比(PSNR)和较好的视觉效果。
Abstract:
An image denoising method based on the statistical model for wavelet coefficients is proposed. It uses a two-dimensional Generalized Autoregressive Conditional Heteroscedasticity (2D-GARCH) model for modeling the wavelet coefficients. A novel wavelet coefficients model is also used to make better use of the important characteristics of wavelet coefficients such as "sharp peak and heavy tailed" marginal distribution and the dependencies between the coefficients. It utilizes maximum likelihood estimation based on fruit fly optimization algorithm (ML Estimation based on FOA) to estimate the model parameters instead of using traditional linear programming in order to improve the accuracy of the modeling. The minimum mean square error estimation (MMSE Estimation) is applied to estimating the parameters of the wavelet coefficients of the original image that is not affected by noise. Experimental results showed that compared to the present widely-used denoising methods the proposed method is more effective in image denoising, and it may achieve higher peak signal-to-noise ratio (PSNR) and good visuality.

参考文献/References:

[1] SIMONCELLI E P. Modeling the joint statistics of image in the wavelet domain[C]//SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics.Denver, USA, 1999: 188-195.
[2] ANTONIADIS A, BIGOT J, SAPATINAS T. Wavelet estimators in nonparametric regretssion: A comparative simulation study[J]. Journal of Statistical Software, 2001, 6(6): 1-83.
[3] DOHONO D L. Denoisingby soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[4] CHANG S, YU B, VATTERELI M. Wavelet thresholding for multiple noisy image[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1631-1635.
[5] CHANG S, YU B, VATTERELI M. Spatially adaptive wavelet thresholding with context modeling for imaged noising[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1522-1531.
[6] CHANG S, YU B, VATTERELI M. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1546.
[7] ACHIM A, BEZERIANOS A, TSAKALIES P. SAR image denoising via Bayesian wavelet shrinkage based on heavy tailed modeling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(8): 1773-1784.
[8] BOLLERSLEV T. Generalized autoregressive conditional heteroscedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327.
[9] NOIBOAR A, COHEN I. Two-dimensional GARCH model with application to anomaly detection[C]//13th European Signal Processing Conference. Istanbul, Turkey, 2005: 1594-1597.
[10] AMIRMAZLAGHANI M, AMIRNDAVAR H. Speckle suppression in SAR image using the 2D GARCH model[J]. IEEE Transactions on Image Processing, 2009, 18(2): 250-259.
[11] AMIRMAZLAGHANI M, AMIRNDAVAR H. Two novel Bayesian multiscale approaches for speckle suppression in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 40 (7): 2980-2993.
[12] PAN W T. A new fruit fly optimization algorithm: taking the financial distress model as an example[J]. Knowledge Based Systems, 2012, 26(2): 69-74.
[13] DOHONO D L, JOHNSTONE I M. Ideal spatial adaptati on via wavelet shrinkage[J]. Biometrika, 1994, 81(3):425-455.
[14] LEE J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, 2(2): 165-168.
[15] 胡海平,莫玉龙.基于贝叶斯估计的小波阈值图像降噪方法[J].红外与毫米波学报, 2002, 21(1): 74-76.HU Haiping, MO Yulong. Method of wavelet threshold denoising based on bayesian esitimation[J].Journal of Infrared and Millimeter Waves, 2002, 42(9): 74-76.
[16] 谢杰成,张大力,徐文立.一种小波去噪方法的几点改进[J].清华大学学报:自然科学版,2002,42(9):1269-1272.XIE Jiecheng, ZHANG Dali, XU Wenli. Several improvements for a wavelet denoising method[J]. Journal of Tsinghua University:Science and Technology, 2002, 42(9): 1269-1272.

相似文献/References:

[1]郭晓霞,杨慧中.小波去噪中软硬阈值的一种改良折衷法[J].智能系统学报,2008,3(03):222.
 GUO Xiao-xia,YANG Hui-zhong.An improved compromise for soft/hard thresholds in wavelet denoising[J].CAAI Transactions on Intelligent Systems,2008,3(01):222.
[2]张毅,罗明伟,罗元.脑电信号的小波变换和样本熵特征提取方法[J].智能系统学报,2012,7(04):339.
 ZHANG Yi,LUO Mingwei,LUO Yuan.EEG feature extraction method based on wavelet transform and sample entropy[J].CAAI Transactions on Intelligent Systems,2012,7(01):339.
[3]徐丽莎,钱晓山,阳春华.结合GM(1,1)和LSSVM的多效蒸发过程参数预测[J].智能系统学报,2012,7(05):462.
 XU Lisha,QIAN Xiaoshan,YANG Chunhua.Parameter prediction of multieffect evaporation process combining GM(1,1) with LSSVM[J].CAAI Transactions on Intelligent Systems,2012,7(01):462.
[4]李洋,焦淑红,孙新童.基于IHS和小波变换的可见光与红外图像融合[J].智能系统学报,2012,7(06):554.
 LI Yang,JIAO Shuhong,SUN Xintong.Fusion of visual and infrared images based on IHS and wavelet transforms[J].CAAI Transactions on Intelligent Systems,2012,7(01):554.

备注/Memo

备注/Memo:
收稿日期:2014-3-25;改回日期:。
基金项目:国家“863”计划资助项目(2012AA112312).
作者简介:李俊泽,男,1988年生,硕士研究生,主要研究方向为图像处理;袁小芳,男,1979年生,副教授,主要研究方向为智能控制理论与应用、电动汽车控制、新能源发电,发表学术论文30余篇;张振军,男,1981年生,讲师,硕士生导师,主要研究方向为机器视觉与智能交通、大规模机器学习与海量数据分析。
通讯作者:张振军.E-mail:zhenjun@hnu.edu.cn.
更新日期/Last Update: 2015-06-16