Wavelet-Based Multi-Channel Image Denoising Using Fuzzy Logic

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.