A New Dual Tree Wavelet Based Image Denoising Using Fuzzy Shrink and Lifting Scheme
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Images are often corrupted by noise owing to channel communication errors, defective image acquisition plans or devices, engine sparks, power interference and atmospheric electrical emissions. In proposed method a new wavelet shrinkage algorithm based on fuzzy logic and the lifting scheme is used. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This fuzzy feature differentiates between important coefficients, namely, image discontinuity coefficients and noisy coefficients. The same is used for enhancing wavelet coefficients' information in the shrinkage step, which results in the fuzzy membership function shrinking the wavelet coefficients based on the fuzzy feature. Examine that the proposed image denoising algorithm in the new shiftable and modified version of discrete wavelet transform, is the dual-tree discrete wavelet transform. Also that the lifting scheme is there it allows a faster implementation of the WT and a fully in-place calculation of the WT. In addition, no extra memory is needed and the original signal can be replaced with its WT. The method transforms an image into the wavelet domain using lifting based wavelet filters in the wavelet domain and finally transforms the result into the spatial domain and the fuzzy threshold is used to extract out the Speckle in highest subbands. Experimental Result shows that the resultant enhanced image obtained has a better performance in noise suppression and edge preservation.
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