A New Image Denosing Method via Variable Splitting Gradient Projection for Constrained Total Variation with Sparsity

Fan Liao; Jia Song Wu; Jun Lin Ouyang; Jean Louis Coatrieux; Hua Zhong Shu

doi:10.4028/www.scientific.net/AMM.654.352

Paper Titles

Determining the Region of Single-Target Interest Area by Prediction Method in Wireless Multimedia Sensor Networks
p.327

Online GRF: Semi-Automatically Labeling Objects in Video
p.337

A Fast Image Reconstruction Algorithm for EIT Based on Wavelet Analysis and LSQR
p.341

SS-BMUSIC Algorithm for Space-Time 2D Signal Model
p.346

A New Image Denosing Method via Variable Splitting Gradient Projection for Constrained Total Variation with Sparsity
p.352

Propagation Environment Analysis and Wireless Mesh Network Implementation for Monitoring the Four Rivers (Based Dalsung Weir)
p.359

Research on Mobile Anchor Node Localization Method Based on Hierarchical
p.362

A Detection and Compensation Method for Dynamic Routing and Transmission Delay
p.366

A Multi-Source Dynamic Determination Algorithm for Digital Synchronization Equipment
p.370

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 654A New Image Denosing Method via Variable Splitting...

A New Image Denosing Method via Variable Splitting Gradient Projection for Constrained Total Variation with Sparsity

Abstract:

The total variation (TV) models have been successfully used for image denoising problem, but most of them usually smooth out the details while removing the noise. In this paper, we propose a new image denoising method that incorporates the sparsity priori information into the traditional TV models. We then solve the optimization problem by combining the variable splitting and dual approaches. Our method can be applied to both isotropic and anisotropic TV models. Experimental results show that the proposed method outperforms the existing methods.

You might also be interested in these eBooks

Mechanical and Electronics Engineering VI

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 654)

Pages:

352-356

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.654.352

Citation:

Cite this paper

Online since:

October 2014

Authors:

Fan Liao, Jia Song Wu, Jun Lin Ouyang, Jean Louis Coatrieux, Hua Zhong Shu

Keywords:

Sparsity Priori Information, Splitting Gradient Projection, Total Variation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L. I. Rudin, S. Osher, and E. Fatemi: Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, Vol. 60, No. 1 (1992), pp.259-268.

DOI: 10.1016/0167-2789(92)90242-f

Google Scholar

[2] A. Chambolle: An algorithm for total variation minimization and applications, Journal of Mathematical imaging and vision, Vol. 20, No. 1-2 (2004), pp.89-97.

Google Scholar

[3] A. Chambolle, and T. Pock: A first-order primal-dual algorithm for convex problems with applications to imaging, " Journal of Mathematical Imaging and Vision, Vol. 40, No. 1 (2011), pp.120-145.

DOI: 10.1007/s10851-010-0251-1

Google Scholar

[4] A. Beck, and M. Teboulle: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, Image Processing, IEEE Transactions on, Vol. 18, No. 11 (2009), pp.2419-2434.

DOI: 10.1109/tip.2009.2028250

Google Scholar

[5] L. Wang, L. Xiao, J. Zhang et al. : New image restoration method associated with tetrolets shrinkage and weighted anisotropic total variation, Signal Processing, (2012).

DOI: 10.1016/j.sigpro.2012.09.004

Google Scholar

[6] J. Duan, Y. Liu, and L. Zhang: Bregman Iteration Based Efficient Algorithm for MR Image Reconstruction From Undersampled K-Space Data, Signal Processing Letters, IEEE, Vol. 20, No. 8 (2013), pp.831-834.

DOI: 10.1109/lsp.2013.2268206

Google Scholar

[7] Y. Wang, J. Yang, W. Yin et al.: A new alternating minimization algorithm for total variation image reconstruction, SIAM Journal on Imaging Sciences, Vol. 1, No. 3 (2008), pp.248-272.

DOI: 10.1137/080724265

Google Scholar