Color image deblurring with impulsive noise

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3752 LNCS, Issue , 2005, Pages 49-60

  Bar, Leah ^a   Brook, Alexander ^b   Sochen, Nir ^a   Kiryati, Nahum ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; EDGE DETECTION; IMPULSE NOISE; VARIATIONAL TECHNIQUES;

BELTRAMI FLOW; DEBLURRING; MULTI-CHANNEL IMAGES; ROBUST DATA FIDELITY; TOTAL-VARIATION MUMFORD-SHAH;

COLOR IMAGE PROCESSING;

EID: 33646570565     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11567646_5     Document Type: Conference Paper

References (28)

1. Tikhonov, A.N., Arsenin, V.I.: Solutions of ill-posed problems. Winston (1977)
2. Rudin, L., Osher, S., Fetami, E.: Non linear total variatrion based noise removal algorithms. Physica D 60 (1992) 259-268
3. Nikolova, M.: A variational approach to remove outliers and impulse noise. J. Math. Imaging Vision 20 (2004) 99-120
4. Nikolova, M.: Minimizers of cost-functions involving nonsmooth data-fidelity terms: Application to the processing of outliers. SIAM J. Numer. Anal. 40 (2002) 965-994
5. Deriche, R., Faugeras, O.: Les EDP en traitement des images et vision par ordinateur. Traitement du Signal 13 (1996)
6. Welk, M., Theis, D., Brox, T., Weickert, J.: PDE-based deconvolution with forward-backward diffusivities and diffusion tensors. In: Proceeding of 5th International Conference on Scale Space and PDE Methods in Computer Vision. Volume 3439 of LNCS. (2005) 585-597
7. Bar, L., Sochen, N., Kiryati, N.: Variational pairing of image segmentation and blind restoration. In Pajdla, T., Matas, J., eds.: Proceedings of 8th European Conference on Computer Vision. Volume 3022 of LNCS. (2004) 166-177
8. Blomgren, P., Chan, T.F.: Color TV: total variation methods for restoration of vector-valued images. IEEE Trans. Image Processing 7 (1998) 304-309
9. Barash, D.: One-step deblurring and denoising color images using partial differential equations. Technical Report HPL-2000-102R1, HP Laboratories (2000)
10. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990) 629-639
11. Kaftory, R., Sochen, N., Zeevi, Y.Y.: Color image denoising and blind deconvolution using the Beltrami operator. In: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis. Volume 1. (2003) 1-4
12. Hunt, B., Kübler, O.: Karhunun-Loeve multispectral image restoration, part I: Theory. IEEE Trans. Acoustics, Speech and Signal Proc. 32 (1984) 592-600
13. Banham, M., Katsaggelos, A.: Digital image restoration. IEEE Signal Processing Mag. 14 (1997) 24-41
14. Molina, R., Mateos, J., Katsaggelos, A.K., Vega, M.: Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Trans. Image Proc. 12 (2003) 1642-1654
15. Bar, L., Sochen, N., Kiryati, N.: Image deblurring in the presence of salt-and-pepper noise. In: Proceeding of 5th International Conference on Scale Space and PDE Methods in Computer Vision. Volume 3439 of LNCS. (2005) 107-118
16. Brook, A., Kimmel, R., Sochen, N.: Variational restoration and edge detection for color images. J. Math. Imaging Vision 18 (2003) 247-268
17. Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE Trans. Image Proc. 7 (1998) 310-318
18. Tschumperlé, D.: PDE's based regularization of multivalued images and applications. PhD thesis, University of Nice-Sophia Antipolis (2002)
19. Kimmel, R., Malladi, R., Sochen, N.: Images as embedded maps and minimal surfaces: Movies, color, texture, and volumetric medical images. Int. J. Computer Vision 39 (2000) 111-129
20. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42 (1989) 577-685
21. Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via Γ-convergence. Comm. Pure Appl. Math. 43 (1990) 999-1036
22. Shah, J.: A common framework for curve evolution, segmentation and anisotropic diffusion. In: IEEE Conference on Computer Vision and Pattern Recognition. (1996) 136-142
23. 1-norm of the gradient and their approximation. Interfaces and Free Boundaries 1 (1999) 17-37
24. Strong, D., Chan, T.: Edge-preserving and scale dependent properties of total variation regularization. CAM Report 00-38, UCLA Math department (2000)
25. Vogel, C.R., Oman, M.E.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Proc. 7 (1998) 813-824
26. Charbonnier, P., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Deterministic edgepreserving regularization in computed imaging. IEEE Trans. Image Proc. 6 (1997) 298-311
27. Geman, S., McClure, D.E.: Bayesian image analysis: An application to single photon emission tomography. Proc. Amer. Statist. Assoc. Statistical Computing Section (1985) 12-18
28. Teboul, S., Blanc-Féraud, L., Aubert, G., Barlaud, M.: Variational approach for edge-preserving regularization using coupled PDE's. IEEE Trans. Image Proc. 7 (1998) 387-397