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Non-negatively constrained image deblurring with an inexact interior point method

Authors
Journal
Journal of Computational and Applied Mathematics
0377-0427
Publisher
Elsevier
Publication Date
Volume
231
Issue
1
Identifiers
DOI: 10.1016/j.cam.2009.02.020
Keywords
  • Image Deblurring
  • Deconvolution Methods
  • Interior Point Algorithms
  • Regularization Techniques
Disciplines
  • Computer Science
  • Design

Abstract

Abstract Nonlinear image deblurring procedures based on probabilistic considerations have been widely investigated in the literature. This approach leads to model the deblurring problem as a large scale optimization problem, with a nonlinear, convex objective function and non-negativity constraints on the sign of the variables. The interior point methods have shown in the last years to be very reliable in 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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