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Replacing the lscr<sup>2</sup> data fidelity term of the standard total variation (TV) functional with an lscr<sup>1</sup> data fidelity term has been found to offer a number of theoretical and practical benefits. Efficient algorithms for minimizing this lscr<sup>1</sup>-TV functional have only recently begun to be developed, the fastest of which exploit(More)
This report discusses methods for estimating linear motion blur. The blurred image is modeled as a convolution between the original image and an unknown point-spread function. The angle of motion blur is estimated using three different approaches. The first employs the cepstrum, the second a Gaussian filter, and the third the Radon transform. To estimate(More)
Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the(More)
Fractal image compression is a technique based on the representation of an image by a contractive transform, on the space of images, for which the fixed point is close to the original image. This broad principle encompasses a very wide variety of coding schemes, many of which have been explored in the rapidly growing body of published research. While(More)
Total Variation (TV) regularization is a popular method for solving a wide variety of inverse problems in image processing. In order to optimize the reconstructed image, it is important to choose a good regularization parameter. The Unbiased Predictive Risk Estimator (UPRE) has been shown to give a good estimate of this parameter for Tikhonov(More)
When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative(More)
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard lscr<sup>2</sup>data fidelity term with an lscr<sup>1</sup> norm. We propose a simple but very flexible method for solving a(More)
We present an efficient algorithm for computing sparse representations whose nonzero coefficients can be divided into groups, few of which are nonzero. In addition to this group sparsity, we further impose that the nonzero groups themselves be sparse. We use a nonconvex optimization approach for this purpose, and use an efficient ADMM algorithm to solve the(More)
—We derive a class of of algorithms for detecting anomalous changes in hyperspectral image pairs by modeling the data with elliptically-contoured (EC) distributions. These algorithms are generalizations of well-known detectors that are obtained when the EC function is Gaussian. The performance of these EC-based anomalous change detectors is assessed on real(More)