In this paper we propose a two-step filter for removing salt-and-pepper impulse noise. In the first phase, a Naive Bayesian Network is used to identify pixels, which are likely to be contaminated by noise (noise candidates). In the second phase, the noisy pixels are restored accrding to a regularization method (based on the optimization of a convex functional) to apply only to those selected noise candidates. The proposed method shows a significant improvement compared to other non linear filters or regularization methods in terms of image details preservation and noise reduction. Our algorithm is also able to remove salt-and-pepper-noise with high noise levels since 70% until 90%. © 2008 IEEE

Bayesian networks for edge preserving salt and pepper image denoising

GIORDANO, Daniela;SPAMPINATO, CONCETTO
2008-01-01

Abstract

In this paper we propose a two-step filter for removing salt-and-pepper impulse noise. In the first phase, a Naive Bayesian Network is used to identify pixels, which are likely to be contaminated by noise (noise candidates). In the second phase, the noisy pixels are restored accrding to a regularization method (based on the optimization of a convex functional) to apply only to those selected noise candidates. The proposed method shows a significant improvement compared to other non linear filters or regularization methods in terms of image details preservation and noise reduction. Our algorithm is also able to remove salt-and-pepper-noise with high noise levels since 70% until 90%. © 2008 IEEE
2008
978-142443322-3
Naive Bayesian Networks; Non-linear filters; Image processing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/86045
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