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PROJECT TITLE :

An enhanced denoising technique using dual tree complex wavelet transform - 2016

ABSTRACT:

This paper, describes the look of Hilbert rework wavelet bases using filters satisfying simultaneous magnitude and [*fr1] sample delay constraints. These bases are crucial in the look and implementation of Dual Tree Advanced Wavelet Rework, DTCWT, or simply referred to as DDWT and is characterised by shift invariance features. Next, the DDWT is employed in image de-noising. In this respect, the DDWT wavelet coefficient matrices of the upper and lower trees are thersholded over two steps. In the first step, these coefficients are thresholed using their Hidden Markov Model HMM representation. Within the second step, the thresholding levels are optimally chosen to reduce a selected objective function of the entire variation of the de-noised image. Many illustrative examples are given to demonstrate the superiority of the proposed technique in comparison with alternative revealed approaches.


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Delays Hidden Markov Models Image Denoising Wavelet Transforms Hilbert Transforms
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