Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic...

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Bibliographic Details
Published in:Pattern recognition Vol. 44; no. 6; pp. 1312 - 1326
Main Authors: Yun, Sangwoon, Woo, Hyenkyun
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.06.2011
Elsevier
Subjects:
Algorithms
Alternating minimization
Applied sciences
Convex optimization
Deconvolution
Exact sciences and technology
Image processing
Information, signal and communications theory
Mathematical models
Motion deblurring
Nonlocal
Operators
Regularization
Signal processing
State of the art
Telecommunications and information theory
Television
Texture
Total variation
Deconvolution
Nonlocal
Convex optimization
Total variation
Alternating minimization
Regularization
Motion deblurring
Performance evaluation
State of the art
Image processing
Image restoration
Algorithm
Blurred image
Convex programming
Texture
Television
Numerical simulation
Ill posed problem
Linearization
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic textures and local geometric information better than local TV. But, since nonlocal TV requires weighted difference between pixels in the whole image, it demands much more computational resources than local TV. By using the linearization of the fidelity term and the proximal function, our proposed algorithm does not require any inversion of blurring operator and nonlocal operator. Therefore, the proposed algorithm is very efficient for motion deblurring problems. We compare the numerical performance of our proposed algorithm with that of several state-of-the-art algorithms for deblurring problems. Our numerical results show that the proposed method is faster and more robust than state-of-the-art algorithms on motion deblurring problems.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2010.12.013