Abstract
Impulse noise is the most common types of noise; it degrades the quality of images and must be removed before performing any high level image processing. In this work, we have proposed a hybrid impulse noise filter, it is implemented in two phases, in the first phase, fuzzy rules are used to detect the pixels affected by impulse noise and in the second phase, artificial neural network is used to remove noise from the affected pixel. The proposed filter is comparatively evaluated with some of the popular impulse noise filter based on peak signal-to-noise ratio and edge preservative factor, it was found that the proposed filter reduces impulse noise and simultaneously preserves image details. For highly corrupted images, the proposed filter can be used, recursively.
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Amitab, K., Medhi, K., Kandar, D., Paul, B.S. (2018). Impulse Noise Reduction in Digital Images Using Fuzzy Logic and Artificial Neural Network. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_14
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DOI: https://doi.org/10.1007/978-981-10-6890-4_14
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