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EnglishNonlinear image deblurring procedures based on probabilistic considerations have been widely investigated in literature. This approach leads to model the deblurring problem as a large scale optimization problem, with a nonlinear, convex objective function and nonnegativity constraints on the sign of the variables. The interior point methods have shown in the last years to be very reliable on the nonlinear programs. In this paper we propose an inexact Newton interior point (IP) algorithm designed for the solution of the deblurring problem. The numerical experience compares the IP method with another state-of-the-art method, the Lucy Richardson algorithm, and shows a significant improvement of the processing time.
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| Data di pubblicazione: | 2009 | |
| Titolo: | Nonnegatively constrained image deblurring with an inexact interior point method | |
| Autori: | S. Bonettini; T. Serafini | |
| Rivista: | JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | |
| Parole Chiave: | Image deblurring; deconvolution methods; interior point algorithms; regularization techniques | |
| Abstract: | Nonlinear image deblurring procedures based on probabilistic considerations have been widely investigated in literature. This approach leads to model the deblurring problem as a large scale optimization problem, with a nonlinear, convex objective function and nonnegativity constraints on the sign of the variables. The interior point methods have shown in the last years to be very reliable on the nonlinear programs. In this paper we propose an inexact Newton interior point (IP) algorithm designed for the solution of the deblurring problem. The numerical experience compares the IP method with another state-of-the-art method, the Lucy Richardson algorithm, and shows a significant improvement of the processing time. | |
| Digital Object Identifier (DOI): | 10.1016/j.cam.2009.02.020 | |
| Handle: | http://hdl.handle.net/11392/534085 | |
| Appare nelle tipologie: | 03.1 Articolo su rivista |
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