Abstract
In this paper, a new technique for automatically identifying the type of noise in digital images has been proposed. Our statistical features based noise Type identification scheme uses machine learning to distinguish different types of noises. Local features of 3x3 window are used to train the machine learning based classifier. Two types of noise (salt & peppers and random-valued) is catered for in this paper. Experiments show that the proposed technique gives promising results and can be enhanced to be a generic noise identification system for every type of noise.
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© 2014 Springer International Publishing Switzerland
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Masood, S., Soto, I., Hussain, A., Jaffar, M.A. (2014). Statistical Features Based Noise Type Identification. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_21
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DOI: https://doi.org/10.1007/978-3-319-13650-9_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13649-3
Online ISBN: 978-3-319-13650-9
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