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In this paper, a new shrink theory and denoising algorithm for image with Gaussian noise based on complex wavelet transform is presented and investigated. We calculate threshold value by a moving window, we can obtain different threshold values for different coefficients using our method. We modify the noisy wavelet coefficients using bivariate shrinkage method, the shrinkage functions do not assume...
The improved multiscale intensity estimation method in wavelet domain presented here is a powerful new tool for image denoising and reconstruction. Specifically, we extend the so-called multiscale intensity estimation in wavelet domain. Unlike traditional wavelet-shrink methods, this method is both well suited to processing Poisson data and capable of preserving image edges. At the heart of this new...
In experiments, observations are often modelled as a noisy signal. If the signal is embedded in an additive Gaussian noise, its estimation is often done by finding a wavelet basis that concentrates the signal energy over few coefficients and by thresholding the noisy coefficients. However, in many problems of physics, the recorded data are not modelled by Gaussian noise but as the realization of a...
When the signal is embedded in an additive Gaussian noise, its estimation is often done by finding a wavelet basis that concentrates the signal energy over few coefficients and by thresholding the noisy coefficients. However, in many practical problems such as medical X-ray image, astronomical and low-light image, the recorded data are not modeled by Gaussian noise but as the realization of a Possion...
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