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The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and(More)
The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of(More)
Acknowledgements First and foremost, I would like to thank my advisor, Professor Martin Vetterli, for sharing his ideas and his infectious enthusiasm. I would also like to thank Ton Kalker for providing some of the building blocks for my MATLAB simulations. The technical content has been in BLOCKINuenced by conversations with
We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared(More)
The diagnosis of infectious diseases has been revolutionized by the development of molecular techniques, foremost with the applications of the polymerase chain reaction (PCR). The achievable high sensitivity and ease with which the method can be used to detect any known genetic sequence have led to its wide application in the life sciences. More recently,(More)
One problem of image interpolation refers to magnifying a small image without loss in image clarity. We propose a wavelet based method which estimates the higher resolution information needed to sharpen the image. This method extrapolates the wavelet transform of the higher resolution based on the evolution of the wavelet transform extrema across the(More)
This correspondence addresses the recovery of an image from its multiple noisy copies. The standard method is to compute the weighted average of these copies. Since the wavelet thresholding technique has been shown to effectively denoise a single noisy copy, we consider in this paper combining the two operations of averaging and thresholding. Because(More)
Some past work has proposed to use lossy compression to remove noise, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. Building on this theme, we explain why compression (via coeecient quantization) is appropriate for ltering noise from signal by making the connection(More)