Paper Info
Title
An EM-based hybrid Fourier-wavelet image deconvolution algorithm
Abstract
Blurred image restoration is a longstanding and critical research problem. We addressed this problem using Expectation Maximization (EM) based approach in wavelet domain. The sparsity property of wavelet coefficients is modeled using the class of Gaussian Scale Mixture (GSM), which represents the heavy-tailed statistical distribution, suitable for natural images. The underlying original image and noise parameters are estimated by alternating EM iterations based on available and hidden data sets, where regularization is introduced using an intermediate variable. Although similar formulations have been proposed before but the resulting optimization problems have been computationally demanding, where our formulation is simple to implement and converge in few iterations. Simulation results are presented to demonstrate the quality of our method both visually and in terms of signal to noise ratio improvement.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738122
Image Processing
Keywords
Field
DocType
image restoration,optimisation,EM-based hybrid Fourier-wavelet image deconvolution algorithm,Gaussian scale mixture,blurred image restoration,expectation maximization based approach,heavy-tailed statistical distribution,signal to noise ratio,sparsity property,wavelet domain,Blur Restoration,Gaussian Scale Mixture,Image Deconvolution
Pattern recognition,Expectation–maximization algorithm,Computer science,Signal-to-noise ratio,Deconvolution,Algorithm,Fourier transform,Regularization (mathematics),Artificial intelligence,Image restoration,Optimization problem,Wavelet
Conference
ISSN
Citations 
PageRank 
1522-4880
1
0.35
References 
Authors
18
2
Name
Order
Citations
PageRank
Muhammad Hanif120725.54
Abd-Krim Seghouane27812.27