A non-local regularization strategy for image deconvolution

Pattern Recognition Letters

Volumn 29, Issue 16, 2008, Pages 2206-2212

  Mignotte, Max ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Image deconvolution or restoration; L curve estimation; Non local regularization; Penalized likelihood

Indexed keywords

BENCHMARKING; CONVOLUTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; NEURAL NETWORKS; PARAMETER ESTIMATION; RESTORATION;

IMAGE DECONVOLUTION OR RESTORATION; L-CURVE ESTIMATION; NON-LOCAL REGULARIZATION; PENALIZED LIKELIHOOD;

REPAIR;

EID: 53949088447     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.08.004     Document Type: Article

References (27)

1. Banham M.R., and Katsaggelos A.K. Spatially adaptive wavelet-based multiscale image restoration. IEEE Trans. Image Process. 5 4 (1996) 619-634
2. Batu, O., Etin, M., 2008. Hyper-parameter selection in advanced synthetic aperture radar imaging algorithms. In: IEEE Conf. on Signal Processing and Communications Applications, Aydin, Turkey.
3. Bioucas-Dias J. Bayesian wavelet-based image deconvolution: A GEM algorithm exploiting a class of heavy-tailed priors. IEEE Trans. Image Process. 15 (2006) 937-951
4. Bioucas-Dias, J., Figueiredo, M., Oliveira, J., 2006. Adaptive total-variation image deconvolution: A majorization-minimization approach. In: Proc. EUSIPCO'2006.
5. Buades A., Coll B., and Morel J.-M. A review of image denoising algorithms, with a new one. SIAM Multiscale Model. Simulat. (SIAM Interdiscip. J.) 4 2 (2005) 490-530
6. Chantas G., Galatsanos N., and Likas A. Bayesian restoration using a new nonstationary edge-preserving image prior. IEEE Trans. Image Process. 15 10 (2006) 2987-2997
7. Charbonnier P., Blanc-Feraud L., Aubert G., and Barlaud M. Deterministic edge-preserving regularization in computed imaging. IEEE Trans. Image Process. 5 12 (1997) 298-311
8. Efros, A.A., Leung, T.K., 1999. Texture synthesis by non-parametric sampling. In: 7th Internat. Conf. on Computer Vision, ICCV'99, Kerkyra, Grece, pp. 1033-1038.
9. Figueiredo M.A.T., and Nowak R.D. An EM algorithm for wavelet-based image restoration. IEEE Trans. Image Process. 12 (2003) 906-916
10. Figueiredo, M., Nowak, R., 2005. A bound optimization approach to wavelet-based image deconvolution. In: IEEE Internat. Conf. on Image Processing -ICIP'05, vol. II, pp. 782-785.
11. Foi, A., Dabov, K., Katkovnik, V., Egiazarian, K., 2006. Shape-adaptive DCT for denoising and image reconstruction. In: Proc. SPIE Electronic Imaging 2006, Image Processing: Algorithms and Systems V, vol. 6064A-18.
12. Guerrero-Colon, J.A., Portilla, J., 2006. Deblurring-by-denoising using spatially adaptive gaussian scale mixtures in overcomplete pyramids. In: IEEE Internat. Conf. on Image Processing, ICIP'06, vol. I, pp. 625-628.
13. Hansen P. Analysis of discrete ill-posed problems by means of the L-curve. SIAM J. Sci. Comput. 14 6 (1992) 1487-1503
14. Jalobeanu A., Blanc-Féraud L., and Zerubia J. Hyperparameter estimation for satellite image restoration using a MCMC maximum likelihood method. Pattern Recognition 32 2 (2002) 341-352
15. Jalobeaunu, A., Kingsbury, N., Zerubia, J., 2001. Image deconvolution using hidden Markov tree modeling of complex wavelet packets. In: IEEE Internat. Conf. on Image Processing, ICIP-2001.
16. Lie, J., 2005. Inverse problems and regularization methods. University of Bergen, Technical report. Available from: .
17. Liu, J., Moulin, P., 1998. Complexity-regularized image restoration. In: IEEE Internat. Conf. on Image Processing, ICIP'98, pp. 555-559.
18. May, K., Stathaki, T., Constantinides, A.G., Katsaggelos, A.K., 1998. Iterative determination of local bound constraints in iterative image restoration. In: IEEE Internat. Conf. on Image Processing, ICIP'98, vol. 2, pp. 833-836.
19. Mignotte M. A segmentation-based regularization term for image deconvolution. IEEE Trans. Image Process. 15 7 (2006) 1973-1984
20. Molina R., Katsaggelos A.K., Mateos J., Hermoso A., and Segall C.A. Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound Gauss-Markov random fields. Pattern Recognition 33 4 (2000) 555-571
21. Neelamani R., Choi H., and Baraniuk R. Forward: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Trans. Signal Process. 52 2 (2004) 418-433
22. Ng D., and Allebach J.P. A subspace matching color filter design methodology for a multispectral imaging system. IEEE Trans. Image Process. 15 9 (2006) 2631-2643
23. Sullivan, B.J., Chang, H.-C., 1991. A generalized landweber iteration for ill-conditioned signal restoration. In: IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, ICASSP'91, pp. 1729-1732.
24. Thompson A.M., Brown J., Kay J., and Titterington D.M. A study of methods of choosing the smoothing parameter in image restoration by regularization. IEEE Trans. Pattern Anal. Machine Intell. 13 4 (1991) 326-339
25. Xu P. Truncated SVD method for discrete ill-posed problems. Geophys. J. Internat. 135 (1998) 505-514
26. Younes L. Parametric inference for imperfectly observed Gibbsian fields. Prob. Th. Fields vol. 82 (1989), Springer-Verlag pp. 625-645
27. Zhou Z., Leahy R.M., and Qi J. Approximate Maximum Likelihood estimation for Gibbs priors. IEEE Trans. Image Process. 6 6 (1997) 844-861