Efficient total variation minimization methods for color image restoration

IEEE Transactions on Image Processing

Volumn 17, Issue 11, 2008, Pages 2081-2088

  Wen, You Wei ^a,b   Ng, Michael K ^c   Huang, Yu Mei ^c,d  

^a SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^d LANZHOU UNIVERSITY   (China)

Author keywords

Color image restoration; Deblurring; Denoising; Total variation (TV)

Indexed keywords

BOOLEAN FUNCTIONS; COLOR IMAGE PROCESSING; IMAGE RECONSTRUCTION; OPTICAL PROPERTIES; OPTIMIZATION; REPAIR; RESTORATION; WAVELET ANALYSIS;

ALTERNATING MINIMIZATION ALGORITHMS; COLOR IMAGE RESTORATION; COLOR IMAGE RESTORATIONS; COLOR IMAGES; DE-NOISE; DEBLURRING; DENOISING; EFFICIENT; TOTAL VARIATION (TV); TOTAL VARIATION MINIMIZATIONS;

COLOR;

ALGORITHM; ARTICLE; ARTIFACT; COLOR; COLORIMETRY; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFACTS; COLOR; COLORIMETRY; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 54949087856     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2008.2003406     Document Type: Article

References (23)

1. l-unified variational framework for image restoration," presented at the Eur. Conf. Computer Vision, Prague, Lecture Notes in Computer Sciences, 2004, vol. 3024, IV.
2. D. Bertsekas, A. Nedic, and E. Ozdaglar, Convex Analysis and Optimization. New York: Athena Scientific, 2003.
3. P. Blomgren and T. Chan, "Color TV: Total variation methods for restoration of vector-valued images," IEEE Trans. Image Process., vol. 7, no. 3, pp. 304-309, Mar. 1998.
4. K. Boo and N. Bose, "Multispectral image restoration with multisensors," IEEE Trans. Geosci. Remote Sens., vol. 35, no. 5, pp. 1160-1170, Sep. 1997.
5. X. Bresson and T. Chan, "Fast minimization of the color total variation norm and application to color image processing," CAM Rep., Aug. 2007, 07-25.
6. J. Carter, "Dual Methods for Total Variation-Based Image Restoration," Ph.D. dissertation, Univ. California, Los Angeles, 2002.
7. A. Chambolle, "An algorithm for total variation minimization and applications," J. Math. Imag. Vis., vol. 20, pp. 89-97, 2004.
8. P. Combettes and V. Wajs, "Signal recovery by proximal forward-backward splitting," SIAM J. Multi. Model. Simul., vol. 4, pp. 1168-1200, 2005.
9. H. Fu, M. Ng, and J. Barlow, "Structured total least squares for color iamge restoration," SIAM J. Sci. Comput., vol. 28, pp. 1100-1119, 2006.
10. N. Galatsanos and R. Chin, "Digital restoration of multichannel images," IEEE Trans. Acoust. Speech Signal Process., vol. 37, no. 3, pp. 415-421, Mar. 1989.
11. N. Galatsanos, A. Katsaggelos, R. Chin, and A. Hillery, "Least squares restoration of multichannel images," IEEE Trans. Signal Process., vol. 39, no. 10, pp. 2222-2236, Oct. 1991.
12. B. Hunt and O. Kubler, "Karhunen-leove multispectral image restoration, part i: Theory," IEEE Trans. Acoust. Speech Signal Process., vol. ASSP-32, no. 3, pp. 592-600, Jun. 1984.
13. D. Krishnan, P. Lin, and X. Tai, "An efficient operator splitting method for noise removal in images," Commun. Comput. Phys., vol. 1, pp. 847-858, 2006.
14. M. Lysaker and X. Tai, "Noise removal using smoothed normals and surface fitting," IEEE Trans. Image Process., vol. 13, no. 10, pp. 1345-1357, Oct. 2004.
15. M. Ng, Iterative Methods for Toeplitz Systems. Oxford, U.K.: Oxford Univ. Press, 2004.
16. M. Ng and N. Bose, "Fast color image restoration with multisensors," Int. J. Imag. Syst. Technol., vol. 12, pp. 189-197, 2002.
17. M. Ng, R. Chan, and W. Tang, "A fast algorithm for deblurring models with neumann boundary conditions," SIAM J. Sci. Comput., vol. 21, pp. 851-866, 2000.
18. M. Ng and W. Kwan, "Comments on least squares restoration of multichannel images," IEEE Trans. Signal Process., vol. 49, no. 11, p. 2885, Nov. 2001.
19. M. Ng, L. Qi, Y. Yang, and Y. Huang, "On semismooth Newton's methods for total variation minimization," J. Math. Imag. Vis., vol. 27, pp. 265-276, 2007.
20. Z. Opial, "Weak convergence of the sequence of successive approximations for nonexpansive mappings," Bull. Amer. Math. Soc. vol. 73, pp. 591-597, 1967.
21. L. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Phys. D, vol. 60, pp. 259-268, 1992.
22. A. Tekalp and G. Pavlovic, "Multichannel image modeling and kalman filtering for multispectral image restoration," Signal Process., vol. 19, pp. 221-232, 1990.
23. C. Vogel and M. Oman, "Iterative method for total variation denoising," SIAM J. Sci. Comput., vol. 17, pp. 227-238, 1996.