Optical spectropolarimetry with incomplete data sets

Optical spectropolarimetry with incomplete data sets

Context. Linear spectropolarimetry is a “photon-hungry” observing technique, requiring a specific sequence of observations to determine the Stokes Q and U parameters. For dual-beam spectropolarimeters, the Q and U Stokes parameters can be ideally determined using observations at N=2 retarder plate positions. The additional polarization signal introduced by instrumental effects requires the redu...

Image Reconstruction from Incomplete Data

Fluorescent optical tomography with large data sets.

In recent years, optical tomography (OT) of highly scattering biological samples has increasingly relied on noncontact CCD-based imaging devices that can record extremely large data sets, with up to 10(9) independent measurements per sample. Reconstruction of such data sets requires fast algorithms. The latter have been developed and applied experimentally in our previous work to imaging of the...

Optical Diffusion Tomography with Large Data Sets

We discuss image reconstruction algorithms for diffuse optical tomography that allow utilization of extremely large data sets. Image reconstruction is performed with experimental data obtained with the use of a CCD camera-based noncontact imager. We demonstrate that more than 10 measurements can be acquired and utilized. This is two orders of magintude or more larger than the data sets which ar...

Image Reconstruction from Incomplete Data