A new approach to constrained total least squares image restoration.
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)...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2000 |
| País: | Brasil |
| Institución: | Universidade Federal de Ouro Preto (UFOP) |
| Repositorio: | Repositório Institucional da UFOP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufop.br:123456789/4660 |
| Acceso en línea: | http://www.repositorio.ufop.br/handle/123456789/4660 https://doi.org/10.1016/S0024-3795(00)00115-4 |
| Access Level: | acceso abierto |
| Palabra clave: | Constrained total least squares Toeplitz matrix Neumann boundary condition Deconvolution Regularization |
| Sumario: | 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. |
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