Defocused image restoration using RBF network and Kalman filter

Author

Jiang, Yugang ; Wu, Qing ; Guo, Ping

Author_Institution

Dept. of Comput. Sci., Beijing Normal Univ., China

Volume

3

fYear

2005

fDate

10-12 Oct. 2005

Firstpage

2507

Abstract

A novel defocused image restoration technique is proposed, which is based on radial basis function (RBF) neural network and Kalman filter. In this technique, firstly a RBF neural network is trained in wavelet domain to estimate defocus parameter. After obtaining the point spread function (PSF) parameter, Kalman filter is adopted to complete the restoration. We experimentally illustrate its performance on simulated data and compare it with other methods. Results show that the proposed PSF parameter estimation technique is more robust to noise.

Keywords

Kalman filters; image restoration; learning (artificial intelligence); optical transfer function; radial basis function networks; wavelet transforms; Kalman filter; artificial intelligence learning; defocused image restoration; parameter estimation; point spread function; radial basis function neural network; wavelet transform; Degradation; Discrete wavelet transforms; Frequency estimation; Image restoration; Neural networks; Optical microscopy; Optical noise; Parameter estimation; Radial basis function networks; Wavelet domain; Defocused Image Restoration; Kalman Filter; Neural Network; Wavelet Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2005 IEEE International Conference on

Print_ISBN

0-7803-9298-1

Type

conf

DOI

10.1109/ICSMC.2005.1571525

Filename

1571525