Abstract
In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic for multi-channel image denoising. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. Besides this fuzzy feature, we use inter-relation between different channels for improving the denoising performance compared to denoising each channel, separately. Then, we use the Takagi-Sugeno model based on two fuzzy features for shrinking wavelet coefficients. We examine our multi-channel image denoising algorithm in the dual-tree discrete wavelet transform domain, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithms indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.
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Saeedi, J., Abedi, A. (2010). Wavelet-Based Multi-Channel Image Denoising Using Fuzzy Logic. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds) Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol 6134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13681-8_6
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DOI: https://doi.org/10.1007/978-3-642-13681-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13680-1
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