Abstract
This paper proposes a new filtering scheme for eliminating random-valued impulse noise from gray images. In the first phase a noise detector is utilized to extract the noise candidates. Next, the algorithm applies a connected component analysis in order to gather the neighboring noisy pixels into separate sets of connected noise candidates. The corrupted pixels are restored using a detail preserving regularization method. The main idea of the proposed approach is to gather the noisy candidate pixels into separate sets of connected pixels and solve the minimization functional over these pixels. Experimental results illustrate the efficiency and effectiveness of the algorithm.
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Kwolek, B. (2007). Detail-Preserving Regularization Based Removal of Impulse Noise from Highly Corrupted Images. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_67
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DOI: https://doi.org/10.1007/978-3-540-71629-7_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
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