TY - JOUR
T1 - A new approach to constrained total least squares image restoration
AU - NG, Kwok Po
AU - Plemmons, Robert J.
AU - Pimentel, Felipe
N1 - Funding Information:
Keywords: Constrained total least squares; Toeplitz matrix; Neumann boundary condition; Deconvolution; Regularization ø The first and third authors would like to dedicate their work on this paper to Prof. Robert J. Plem-mons, in celebration of his 60th birthday. ∗ Corresponding author. Tel.: +852-28592252; fax: +852-25592225. E-mail addresses: [email protected] (M.K. Ng), [email protected] (R.J. Plemmons). 1 Research supported in part by Research Grants Council grant no. HKU 7147/99P and HKU CRCG grant no. 10202720. 2 Research supported in part by the National Science Foundation under grant no. CCR-9732070. 3 Research supported by Brazilian Government/CAPES in cooperation with Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, USA.
PY - 2000/9/1
Y1 - 2000/9/1
N2 - Recently there has been a growing interest and progress in using total least squares (TLS) methods for solving blind deconvolution problems arising in image restoration. Here, the true image is to be estimated using only partial information about the blurring operator, or point spread function (PSF), which is subject to error and noise. In this paper, we present a new iterative, regularized, and constrained TLS image restoration algorithm. Neumann boundary conditions are used to reduce the boundary artifacts that normally occur in restoration processes. Preliminary numerical tests are reported on some simulated optical imaging problems in order to illustrate the effectiveness of the approach, as well as the fast convergence of our iterative scheme.
AB - Recently there has been a growing interest and progress in using total least squares (TLS) methods for solving blind deconvolution problems arising in image restoration. Here, the true image is to be estimated using only partial information about the blurring operator, or point spread function (PSF), which is subject to error and noise. In this paper, we present a new iterative, regularized, and constrained TLS image restoration algorithm. Neumann boundary conditions are used to reduce the boundary artifacts that normally occur in restoration processes. Preliminary numerical tests are reported on some simulated optical imaging problems in order to illustrate the effectiveness of the approach, as well as the fast convergence of our iterative scheme.
KW - Constrained total least squares
KW - Deconvolution
KW - Neumann boundary condition
KW - Regularization
KW - Toeplitz matrix
UR - http://www.scopus.com/inward/record.url?scp=0034415725&partnerID=8YFLogxK
U2 - 10.1016/S0024-3795(00)00115-4
DO - 10.1016/S0024-3795(00)00115-4
M3 - Journal article
AN - SCOPUS:0034415725
SN - 0024-3795
VL - 316
SP - 237
EP - 258
JO - Linear Algebra and Its Applications
JF - Linear Algebra and Its Applications
IS - 1-3
ER -