Simultaneous Blur Identification And Image Restoration Using The EM Algorithm

1989 ◽  
Author(s):  
A. K. Katsaggelos ◽  
K. T. Lay
Keyword(s):  
Em Algorithm ◽  
Image Restoration ◽  
The Em Algorithm ◽  
Blur Identification

scholarly journals Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lijuan Zhang ◽  
Dongming Li ◽  
Wei Su ◽  
Jinhua Yang ◽  
Yutong Jiang
Keyword(s):  
Em Algorithm ◽  
Image Restoration ◽  
Adaptive Optics ◽  
Expectation Maximization ◽  
Imaging System ◽  
Phase Error ◽  
Estimation Accuracy ◽  
Parameter Estimations ◽  
Power Spectral ◽  
The Em Algorithm

To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

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