Total variation-based image restoration using I-divergence

International Review on Computers and Software

Volumn 8, Issue 2, 2013, Pages 668-672

  Wu, Qiufeng ^a,b   Wang, Kuanquan ^a   Zuo, Wangmeng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b NORTHEAST AGRICULTURAL UNIVERSITY   (China)

Author keywords

I divergence; Image restoration; Total variation

Indexed keywords

EID: 84877351954     PISSN: 18286003     EISSN: 18286011     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. W. Liu, An Image Restoration Algorithm based on Image Fusion, International Review on Computers and Software, Vol.7, n. 3, pp. 1239-1244, 2012.
2. N. Khlifa, N. Gribaa, K. Hamrouni, Planar Nuclear Images Restoration Based on Fourier and Wavelet Transforms, International Review on Computers and Software, Vol.4, n. 2, pp. 177-182, 2009.
3. L. Rudin, S. Osher, E. Fatemi, Nonlinear Total Variation Based Noise Removal Algorithms, Physica D, Vol. 60, n. 1-4, pp. 259-268, 1992.
4. D. Donoho, I. Johnstone, Ideal Spatial Adaptation via Wavelet Shrinkage, Biometrika, Vol. 81, n. 3, pp. 425-455, 1994.
5. D. Donoho, I. Johnstone, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, Vol. 90, n. 432, pp. 1200-1224, 1995.
6. R. Vohra, A. Tayal, Image Restoration Using Thresholding Techniques on Wavelet Coefficients, International Journal of Computer Science Issues, Vol. 8, n. 3, pp. 400-404, 2011.
7. Peyre, G., Bougleux, S., Cohen, L., Non-local Regularization of Inverse Problems, European Conference on Computer Vision (Page: 57-68 Year of Publication: 2008 ISSN: 0302-9743).
8. S. Wang, Z.W. Liu, W. S. Dong, L. C. Jiao, Q. X. Tang, Total Variation Based Image Deblurring with Nonlocal Self-similarity Constraint, Electronics Letters, Vol.47, n. 16, pp. 916-918, 2011.
9. P. Blomgren, T. F. Chan, Color TV: Total Variation Methods for Restoration of Vector-Valued Images, IEEE Transactions on Image Processing, Vol. 7, n. 3, pp. 304-309, 1998.
10. C. R. Vogel, M. E. Oman, Fast Robust Total Variation-Based Reconstruction of Noisy, Blurred Images, IEEE Transactions on Image Processing, Vol. 7, n. 6, pp. 813-824, 1998.
11. J. Oliveira, J. M. Bioucas-Dia, M. Figueiredo, Adaptive Total Variation Image Deblurring: a Majorization-Minimization Approach, Signal Processing, Vol. 89, n. 9, pp. 1683-1693, 2009.
12. A. Beck, M. Teboulle, Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems, IEEE Transactions on Image Processing, Vol. 18, n. 11, pp. 2419-2434, 2009.
13. A. Beck, M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal of Image Science, Vol. 2, n. 1, pp. 183-202, 2009.
14. J. Bioucas-Das, M. Figueiredo, A new TWIST: Two Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration, IEEE Transactions on Image Processing, Vol. 16, n. 2, pp. 2992-3004, 2007.
15. O.V. Michailovich, An Iterative Shrinkage Approach to Total-Variation Image Restoration, IEEE Transactions on Image Processing, Vol. 20, n. 5, pp. 1281-1299, 2011.
16. W. Zuo, Z. Lin, A Generalized Accelerated Proximal Gradient Approach for Total-Variation-Based Image Restoration, IEEE Transactions on Image Processing, Vol. 20, n. 10, pp.2748-2759, 2011.
17. I. Csiszá, Why Least Squares and Maximum Entropy? An Axiomatic Approach to Inference for Linear Inverse Problems, The Annals of Statistics, Vol. 19, n. 4, pp.2032-2066, 1991.
18. D.L Snyder, T. J. Schulz, J.A.O' Sullivan, Deblurring Subject to Nonnegativity Constraints, IEEE Transactions on Signal Processing, Vol. 40, n. 5, pp.1143-1150, 1992.
19. A. Marquina, S. Osher, Explicit Algorithms for a New Time Dependent Model Based on Level Set Motion for Nonlinear Delburring and Noise Removal, SIAM Journal of Science Computition, Vol. 22, n. 2, pp.387-405, 2000.