A nonlinear entropic variational model for image filtering
EURASIP Journal on Applied Signal Processing
Gauss-Markov Random field model for non-quadratic regularization of complex SAR images
ICOSSSE'08 Proceedings of the 7th WSEAS international conference on System science and simulation in engineering
IEEE Transactions on Image Processing
Fusion and inversion of SAR data to obtain a superresolution image
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Compressed sensing for synthetic aperture radar imaging
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Separate magnitude and phase regularization in MRI with incomplete data: preliminary results
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Enhancement of coupled multichannel images using sparsity constraints
IEEE Transactions on Image Processing
Compressed sensing of complex-valued data
Signal Processing
SAR image reconstruction and autofocus by compressed sensing
Digital Signal Processing
A Bayesian approach to SAR imaging
Digital Signal Processing
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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 SAR techniques, the method we propose produces images with increased resolution, reduced sidelobes, reduced speckle and easier-to-segment regions. Our technique effectively deals with the complex-valued, random-phase nature of the underlying SAR reflectivities. An efficient and robust numerical solution is achieved through extensions of half-quadratic regularization methods to the complex-valued SAR problem. We demonstrate the performance of the method on synthetic and real SAR scenes