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We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction...
We consider the problem of hyper-parameter selection in advanced image reconstruction algorithms used in synthetic aperture radar (SAR) imaging. To deal with the parameter selection problem in these algorithms, we propose the use of unbiased predictive risk estimation and generalized cross-validation techniques. We demonstrate the effectiveness of the applied methods through experiments based on electromagnetically...
Sparse signal representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structured, sparse signal representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy. Around this algorithm, we construct a methodology...
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