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We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this(More)
In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the(More)
We explore the application of a homotopy continuation-based method for sparse signal representation in overcomplete dictionaries. Our problem setup is based on the basis pursuit framework , which involves a convex optimization problem consisting of terms enforcing data fidelity and sparsity, balanced by a regu-larization parameter. Choosing a good(More)
We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional(More)
When segmenting images of low quality or with missing data, statistical prior information about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonparametric shape prior model for image(More)
There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such(More)
We propose a regularization-based method for the complex-valued synthetic aperture radar (SAR) image formation problem. The method can produce images with higher resolution than that supported by the measured data, as well as images with reduced variability of reflectivity magnitudes within homogeneous regions and preserved region boundaries. This is(More)
The success and accuracy of remote sensing with Radar can be predicted from reasonably limited samples of Radar signals. ABSTRACT | Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the(More)
In this paper, we present a novel information theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the(More)