A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preprocessing of video signals for MPEG coding by clustering filter
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Image denoising with neighbour dependency and customized wavelet and threshold
Pattern Recognition
A new fuzzy-based wavelet shrinkage image denoising technique
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Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
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Improved hidden Markov models in the wavelet-domain
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The design of approximate Hilbert transform pairs of wavelet bases
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Shiftable multiscale transforms
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An algorithm for integrated noise reduction and sharpness enhancement
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Image denoising using scale mixtures of Gaussians in the wavelet domain
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A Fuzzy Noise Reduction Method for Color Images
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The SURE-LET Approach to Image Denoising
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SURE-LET Multichannel Image Denoising: Interscale Orthonormal Wavelet Thresholding
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Binarization in Magnetic Resonance Images (MRI) of human head scans with intensity inhomogeneity
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. 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. We use this fuzzy feature for enhancing wavelet coefficients' information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. In addition, we extend our noise reduction algorithm for multi-channel images. We use inter-relation between different channels as a fuzzy feature for improving the denoising performance compared to denoising each channel, separately. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.