Image restoration with a high-order total variation minimization method

In this paper, we propose a fast and efficient way to restore blurred and noisy images with a high-order total variation minimization technique. The proposed method is based on an alternating technique for image deblurring and denoising. It starts by finding an approximate image using a Tikhonov reg...

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Bibliographic Details
Published in:Applied mathematical modelling 2013-09, Vol.37 (16-17), p.8210-8224
Main Authors: Lv, Xiao-Guang, Song, Yong-Zhong, Wang, Shun-Xu, Le, Jiang
Format: Article
Language:English
Subjects:
Algorithms
Deblurring
Denoising
High-order
Image restoration
Mathematical analysis
Mathematical models
Minimization
Noise reduction
Regularization
Television
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Summary:In this paper, we propose a fast and efficient way to restore blurred and noisy images with a high-order total variation minimization technique. The proposed method is based on an alternating technique for image deblurring and denoising. It starts by finding an approximate image using a Tikhonov regularization method. This corresponds to a deblurring process with possible artifacts and noise remaining. In the denoising step, a high-order total variation algorithm is used to remove noise in the deblurred image. We see that the edges in the restored image can be preserved quite well and the staircase effect is reduced effectively in the proposed algorithm. We also discuss the convergence of the proposed regularization method. Some numerical results show that the proposed method gives restored images of higher quality than some existing total variation restoration methods such as the fast TV method and the modified TV method with the lagged diffusivity fixed-point iteration.
ISSN:0307-904X
DOI:10.1016/j.apm.2013.03.028