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This paper present the global generalized cross validation as the appropriate choice of the regularization parameter in the preconditioned Gl-LSQR method in solving image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-LSQR method can give better reconstructions of the true image than other parameters considered in this study.
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참고문헌 (8)
참고문헌 신청
Sei-young Chung / 2009 / RESTORATION OF BLURRED IMAGES BY GLOBAL LEAST SQUARES METHOD / 충청수학회지 22(2):177~186
P. C. Hansen / 1992 / Analysis of Discrete Ill-Posed Problems by means of the L-Curve / SIAM Review 34:561~580
P. C. Hansen / 1998 / Rank-deficient and discrete ill-posed problems / SIAM
P. C. Hansen / 2010 / Discrete Inverse Problems: Insight and Algorithms / SIAM
M. K. Ng / 1999 / A fast algorithm for deblurring models with neumann boundary conditions / SIAM J. Sci. Comp 21(3):851~866
P. C. Hansen / 1992 / Analysis of Discrete Ill-Posed Problems by means of the L-Curve / SIAM Review 34:561~580
P. C. Hansen / 1998 / Rank-deficient and discrete ill-posed problems / SIAM
P. C. Hansen / 2010 / Discrete Inverse Problems: Insight and Algorithms / SIAM
M. K. Ng / 1999 / A fast algorithm for deblurring models with neumann boundary conditions / SIAM J. Sci. Comp 21(3):851~866
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