Abstract
Magnetic resonance images (MRIs) are sensitive to redundant Rician noise. The proposed adaptive hexagonal fuzzy hybrid filtering technique adapts itself to remove Rician noise variances. The removal of noise variance is performed by constructing a hexagonal membership function along with local and nonlocal filters. The statistical feature such as local mean (μ i) and global mean (μ g) is determined to find fuzzy weights by constructing a hexagonal membership function for nonlocal filter to preserve the structural information and for local filter to preserve edges. The restoration is performed by multiplying its corresponding fuzzy weight with the restored image of local and nonlocal filter in order to improve the quality of an image. Detailed simulation is performed for Brain Web database and real MRI images at various noise levels using the proposed adaptive hexagonal fuzzy hybrid filtering algorithm and existing algorithms. The visual and diagnostic qualities of the denoised image are well preserved for the proposed adaptive hexagonal fuzzy hybrid filter both at low and high densities of Rician noise.
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