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Image restoration by cosine transform-based iterative regularization. (English) Zbl 1062.94507

Summary: We consider an ill-posed deconvolution problem with a noise-contaminated observation, and a known convolution kernel. In this paper, we consider the use of the Neumann boundary condition (corresponding to a reflection of the original scene at the boundary). The resulting blurring matrices are block Toeplitz-plus-Hankel matrices with Toeplitz-plus-Hankel blocks. We study the application of the preconditioned iterative regularization scheme for solving these linear systems, where the blurring matrices are approximated by cosine transform preconditioners. We give a simple approach for finding these preconditioners and show how iterations can be effectively and efficiently regularized for solving ill-posed problems by using the spectral decomposition of the preconditioner.

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
65F22 Ill-posedness and regularization problems in numerical linear algebra
Full Text: DOI

References:

[1] Andrew, H.; Hunt, B., Digital Image Restoration (1977), Prentice-Hall: Prentice-Hall New Jersey · Zbl 0379.62098
[2] Banham, M.; Katsaggelos, A., Digital Image Restoration, IEEE Signal Processing Magazine, March, 24-41 (1997)
[3] Bjorck, A., Numerical Methods for Least Squares Problems (1996), SIAM: SIAM Philadelphia · Zbl 0847.65023
[4] Chan, R.; Ng, M., Conjugate gradient methods for toeplitz systems, SIAM Review, 38, 427-482 (1996) · Zbl 0863.65013
[5] Chan, R.; Ng, M.; Plemmons, R., Generalization of Strung’s preconditioner with applications to toeplitz least squares problems, J. Numer. Linear Algebra Appl, 3, 45-64 (1996) · Zbl 0842.65029
[6] Engl, H.; Hanke, M.; Neubauer, A., Regularization of Inverse Problems (1996), Kluwer Academic Publishers: Kluwer Academic Publishers The Netherlands · Zbl 0859.65054
[7] Golub, G.; Van Loan, C., Matrix Computations (1989), The Johns Hopkins University Press: The Johns Hopkins University Press Baltimore, MD · Zbl 0733.65016
[8] Gonzalez, R.; Woods, R., Digital Image Processing (1992), Addison Wesley: Addison Wesley New York
[9] Hanke, M.; Nagy, J., Restoration of atmospherically blurred images by symmetric indefinite conjugate gradient techniques, Inverse Problems, 12, 157-173 (1996) · Zbl 0859.65141
[10] Hanke, M.; Nagy, J.; Plemmons, R., Preconditioned iterative regularization for ill-posed problems, (Reichel, L.; Ruttan, A.; Varga, R., Numerical Linear Algebra (1993), de Gruyter: de Gruyter Berlin), 141-163 · Zbl 0794.65039
[11] Hansen, P., Analysis of discrete ill-posed problems by means of the \(L\)-curve, SIAM Review, 34, 561-580 (1992) · Zbl 0770.65026
[12] Jain, A., Fundamentals of Digital Image Processing (1989), Prentice-Hall: Prentice-Hall Englewood Cliffs, NJ · Zbl 0744.68134
[13] Lagendijk, R.; Biemond, J., Iterative Identification and Restoration of Images (1991), Kluwer Academic Publishers · Zbl 0752.68093
[14] F. Luk, D. Van devoorde, Reducing Boundary Distortion in Image Restoration, Advanced Signal Processing Algorithms, Architectures and Implementations VI, Proceedings of SPIE 2296, 1994; F. Luk, D. Van devoorde, Reducing Boundary Distortion in Image Restoration, Advanced Signal Processing Algorithms, Architectures and Implementations VI, Proceedings of SPIE 2296, 1994
[15] Ng, M.; Chan, R.; Tang, W., A fast algorithm for deblurring models with Neumann boundary conditions, SIAM Journal on Scientific Computing, 21, 851-866 (2000) · Zbl 0951.65038
[16] Rao, K.; Yip, P., Discrete Cosine Transform: Algorithms, Advantages, Applications (1990), Acadamic Press: Acadamic Press Boston · Zbl 0726.65162
[17] Van der Sluis, A.; Van der Vorst, H., SIRT- & CG-type methods for the iterative solution of least squares problems, Linear Algebra and its Applications, 130, 257-302 (1990) · Zbl 0702.65042
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