Abstract
Image acquisition processes usually add some level of noise and degradation, thus causing common problems in image restoration. The restoration process depends on the knowledge about the degradation parameters, which is critical for the image deblurring step. In order to deal with this issue, several approaches have been used in the literature, as well as techniques based on machine learning. In this paper, we presented an approach to identify blur parameters in images using the Optimum-Path Forest (OPF) classifier. Experiments demonstrated the efficiency and effectiveness of OPF when compared against some state-of-the-art pattern recognition techniques for blur parameter identification purpose, such as Support Vector Machines, Bayesian classifier and the k-nearest neighbors.
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Notes
- 1.
All these ranges for both L and \(\sigma \) were empirically chosen.
- 2.
The experiments were conducted on a computer with a Pentium Intel Core i5® 650 3.2 Ghz processor, 4 GB of memory RAM and Linux Ubuntu Desktop LTS 12.04 as the operational system.
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Acknowledgment
The authors are grateful to FAPESP grants #2014/16250-9 and #2014/12236-1, CNPq grant #306166/2014-3, as well as CAPES.
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Pires, R.G., Fernandes, S.E.N., Papa, J.P. (2017). Blur Parameter Identification Through Optimum-Path Forest. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_20
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