VIS_CS - Compressed Sensing image reconstruction algorithm based on visibilities

VIS CS is a Compressed Sensing-based algorithm that reconstructs the X-ray source distribution from the Fourier components measured by RHESSI.

The VIS_CS algorithm uses a custom Gaussian basis on the assumption that sources can be represented as linear combinations of a number of Gaussian distributions. This Gaussian basis is an overcomplete basis consisting of two-dimensional Gaussian distributions of various sizes, orientations, and locations in the xy-plane. VIS_CS randomly samples up to 10^6 basis functions from the infinite set of Gaussian distributions.

VIS_CS produces competitive results with accurate photometry and morphology without requiring any algorithm- and X-ray source-specific parameter tuning. Its robustness and performance make this algorithm well-suited for generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

Using the VIS_CS algorithm at the command line:

Select algorithm  o->set, image_algorithm='vis_cs'or 'vcs' or 'vis_cs' or 'hsi_vis_cs'
Object Class  HSI_VIS_CS
Extract Objectcs_obj = o->get(/obj,class='hsi_vis_cs')  Extract the object used in the hsi_image object, o
Parameter Prefix 

vis_sc, e.g.
sparseness = o->get(/vis_cs_sparseness)
cs_info = o-> get(/vis_cs, /info)
cs_all = o->get(/vis_sc)

All parameter names specific to this algorithm have this prefix
Parameter Table  VIS_CS Object Parameter Table

List and short description of control and info parameters specific to this algorithm