Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar
imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature,
such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop
an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of
sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in
SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of
multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation
of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed
for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the
effectiveness of this approach on various SAR images.
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