Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic 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 feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Reversible phase transformation and doubly-charged anions at the surface of simple cubic RbC60

The simple cubic phase of a RbC60 thin film has been studied using photoelectron spectroscopy. The simple cubic-to-dimer transition is found to be reversible at the film surface. A sharp Fermi edge is observed and a lower limit of 0.5 eV is found for the surface Hubbard U, pointing to a strongly-correlated metallic character of thin-film simple cubic RbC60. A molecular charge state is identified in the valence band and core level photoemission spectra which arises from C602- anions and contributes to the spectral intensity at the Fermi level.Comment: 13 pages, 3 figure

Tomographic inversion using ℓ1\ell_1-norm regularization of wavelet coefficients

We propose the use of $\ell_1$ regularization in a wavelet basis for the solution of linearized seismic tomography problems $Am=d$, allowing for the possibility of sharp discontinuities superimposed on a smoothly varying background. An iterative method is used to find a sparse solution $m$ that contains no more fine-scale structure than is necessary to fit the data $d$ to within its assigned errors.Comment: 19 pages, 14 figures. Submitted to GJI July 2006. This preprint does not use GJI style files (which gives wrong received/accepted dates). Corrected typ