Paper Info
Title
A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution
Abstract
Recently, TV-Stokes model has been widely researched for various image processing tasks such as denoising and inpainting. In this paper, we introduce a new TV-Stokes model for image deconvolution, and propose fast and efficient iterative algorithms based on the augmented Lagrangian method. The new TV-Stokes model is a two-step model: in the first step, a smoothed and divergence free tangential field of the observed image is recovered based on total variation (TV) minimization and a new data fidelity term; in the second step, the image is reconstructed by minimizing the distance between the unit image gradient and the regularized unit normal direction. Numerical experiments demonstrate that the proposed model has the potential to outperform popular TV-based restoration methods in preserving texture details and fine structures. As a result, an improvement in signal-to-noise ratio (SNR) is obtained for deconvolution and denoising results.
Year
DOI
Venue
2012
10.1007/s10915-011-9519-x
J. Sci. Comput.
Keywords
Field
DocType
image denoising,denoising result,two-step model,observed image,tv-stokes model,augmented lagrangian method,new tv-stokes model,unit image gradient,new data,tv-stokes model · total variation · augmented lagrangian method · image deconvolution · image denoising,various image processing task,image deconvolution,total variation
Noise reduction,Computer vision,Mathematical optimization,Image gradient,Blind deconvolution,Non-local means,Deconvolution,Image processing,Inpainting,Augmented Lagrangian method,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
51
3
1573-7691
Citations 
PageRank 
References 
9
0.56
28
Authors
3
Name
Order
Citations
PageRank
Dai-Qiang Chen1928.35
Lizhi Cheng229034.84
Su Fang3615.73