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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.5, No.8, Jun. 2013

A Hybrid Restoration Approach of Defocused Image Using MGAM and Inverse Filtering

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Author(s)

Fenglan Li,Liyun Su,Yun Jiang,Min Sun

Index Terms

Defocused Image Restoration; Wavelet Transform; Multivariate Generalized Additive Model (MGAM); Inverse Filtering (InvF)

Abstract

A novel hybrid restoration scheme of defocused image is presented, which uses multivariate generalized additive model (MGAM) which is a nonparametric statistical regression model with no curse of dimensionality and inverse filtering (InvF). In this algorithm, firstly the five features of wavelet domain in defocused digital image, which are very stable relationship with the point spread function (PSF) parameter, are extracted by training and fitting a multivariate generalized additive model which is to estimate defocused blurred parameter. After the point spread function parameter is obtained, inverse filtering, which is needed to known the point spread function and a non-blind restoration method, is applied to complete the restoration for getting the true image. Simulated and real blurred images are experimentally illustrated to evaluate performances of the presented method. Results show that the proposed defocused image hybrid restoration technique is effective and robust.

Cite This Paper

Fenglan Li,Liyun Su,Yun Jiang,Min Sun,"A Hybrid Restoration Approach of Defocused Image Using MGAM and Inverse Filtering", IJIGSP, vol.5, no.8, pp.22-28, 2013.DOI: 10.5815/ijigsp.2013.08.03

Reference

[1]Su Liyun, Liu Ruihua, Li Fenglan, and Li Jiaojun. Semi-blind deconvolution of defocused image with MCMA. International Conference on Computer Application and System Modeling, Vol, 10, August, Taiyuan, Shanxi, China, 2010, pp:45-49.

[2]Su Liyun and Li Fenglan. Deconvolution of Defocused Image with Multivariate Local Polynomial Regresionand Iterative Wiener Filtering in DWT domain. Mathematical problems in Engineering, Vol. 2010, 2010, pp: 1-14.

[3]Su Liyun, Ma Hong, Li Zheng, Ju Shenggen. Blind image restoration based on constant modulus with averaging and ANFIS. Fourth International Conference on Image andGraphics, 2007, pp: 143-148.

[4]Li Zheng, Su Liyun, Ju Shenggen, Yang Jian. Semi-blind Defocused Image Restoration using BP Neural Network and Inverse Filter in Wavelet Domain. Journal of Sichuan University (Natural Science Edition), 44(1), 2007, pp: 47-53.

[5]Yehong Liao, Xueyin Lin. Blind Image Restoration with Eigen-Face Subspace. IEEE Trans. on Image Processing, 14(11), 2005, pp: 1766-1772.

[6]M. M. Chang, A. M. Tekalp, A. T. Erdem. Blur Identification using the Bi-Spectrum. IEEE Trans. On Image Processing, 39(10), pp: 2323-2325, 1991.

[7]R. L. Lagendijk, J. Biemond, B. E. Boekee. Identification and Restoration of Noisy Blurred Images using the Expectation-Maximization Algorithm. IEEE Trans. on Acoustics, Speech, Signal Processing, 1990, 38(7): 1180-1191.

[8]D. Kundur, D. Hatzinakos. A Novel Blind Deconvolution Scheme for Image Restoration using Recursive Filtering. IEEE Trans. on Signal Processing, 1998, 46(2): 375-390.

[9]A. K. Katsaggelos, K. T. Lay. Maximum Likelihood Blur Identification and Image Restoration using the EM Algorithm. IEEE Trans. on Signal processing, 1991, 39(3) :729-733.

[10]Kantz H and Schreiber T. Nonlinear Time Series Analysis Cambridge University Press, 1997.

[11]Su Liyun, Li Fenglan, Xu Feng, Liu Yuran. Defocused Image Restoration Using RBF Network and Iterative Wiener Filter in Wavelet Domain.2008 International congress on image and signal processing, Sanya Hainan , China, 2008,3: 311-315.

[12]Fan J. and I. Gijbels, Local polynomial modelling and its applications, Chapman and Hall, 1996.

[13]Su Liyun. Prediction of multivariate chaotic time series with local polynomial fitting, Computers & Mathematics with Applications, 2010, 59(2):737-744.

[14]Su Li-yun, Ma Yan-jun, Li Jiao-jun, Application of local polynomial estimation in suppressing strong chaotic noise. Chinese Physics B. 2012, 21(2): 020508.

[15]Su Liyun,Li Fenglan, Deconvolution of defocused image with multivariate local polynomial regression and iterative Wiener filtering in DWT domain. Math Probl Eng, 2010: 605241.

[16]Su Liyun, Zhao Yanyong, and Yan Tianshun, Li Fenglan. Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics, PLoS ONE, 2012,7(9):e43719. 

[17]Su Liyun, Yan Tianshun, Zhao Yanyong, Li Fenglan. Local polynomial regression solution for partial differential equations with initial and boundary values, Discrete Dynamics in Nature and Society, vol. 2012, Article ID 201678, , 2012.

[18]Su Liyun, Yan Tianshun, Zhao Yanyong, Li Fenglan.Local polynomial regression solution for differential equations with initial and boundary values, Discrete Dynamics in Nature and Society, vol. 2013, Article ID 530932, 2013.

[19]Su Liyun, Zhao Yanyong, and Yan Tianshun. Two-stage Method Based on Local Polynomial Fitting for a Linear Heteroscedastic Regression Model and Its Application in Economics, Discrete Dynamics in Nature and Society, vol. 2012, Article ID 696927, 2012.

[20]Paavo Alku. Glottal Wave analysis with Pitch Synchronous Iterative adaptive inverse filtering. Speech Communication, 11 (2-3), pp: 109-118, 1990.