[1]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(1):62-67.[doi:10.3969/j.issn.1673-4785.201403066]
Copy

A method of image denoising based on two-dimensional GARCH model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 62-67 Column: 学术论文—机器感知与模式识别 Public date: 2015-03-25
Title:
A method of image denoising based on two-dimensional GARCH model
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
Keywords:
wavelet transform; statistical modeling; two-dimensional GARCH model; FOA; image denoising
CLC:
TP751.1
DOI:
10.3969/j.issn.1673-4785.201403066
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.
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.
Similar References:

Memo

-

Last Update: 2015-06-16

Copyright © CAAI Transactions on Intelligent Systems