VIS_WV - Finite Isotropic waVElet transform Compressed Sensing (also known as 5-CS or FIVE-CS) image reconstruction algorithm based on visibilities

VIS WV uses compressed sensing realized by means of regularized deconvolution and the Finite Isotropic Wavelet Transform to construct RHESSI images. It solves the following minimization problem: min_x {||B-Hx||^2_2 + \lambda||Wx||_1}

The method utilizes the Finite Isotropic Wavelet Transform with the Meyer function as the mother wavelet. Further, compressed sensing is realized by optimizing a sparsity-promoting regularized objective function by means of the Fast Iterative Shrinkage-Thresholding Algorithm. Eventually, the regularization parameter is selected by means of the Miller criterion.

From the application of the sparsity constraint and the use of a continuous, isotropic framework for the wavelet transform, VIS WV provides notable spatial accuracy and significantly reduces the ringing effects due to the instrument point spread function.

Using the VIS_WV algorithm at the command line:

Select algorithm  o->set, image_algorithm='vis_wv'or 'vwv' or 'vis_wv' or 'hsi_vis_wv'
Object Class  HSI_VIS_WV
Extract Objectwv_obj = o->get(/obj,class='hsi_vis_wv')  Extract the object used in the hsi_image object, o
Parameter Prefix 

vis_wv, e.g.
nscales = o->get(/vis_wv_nscales)
wv_info = o-> get(/vis_wv, /info)
wv_all = o->get(/vis_wv)

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

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