The ability to obtain high quality data in a short amount of time or indeed to recover high resolution images from from incomplete blurred and noisy data can significantly improve the results of experiments potentially leading to new and exciting scientific discoveries. Mathematical techniques such as compressed sensing and sparse regularisation provide these benefits for many sources of data in a variety of different fields. Compressed Sensing for Magnetic Resonance Imaging and Cosmology (COSMIC) is a CEA DRF funded project that brings together MRI experts at NeuroSpin with astronomical image analysis experts at CosmoStat in order to develop an open source software package called PySAP (Python Sparse data Analysis Package) that implements these signal processing tools. This talk will provide a brief introduction to the types of problems encountered in acquiring/processing MRI and astrophysical images and demonstrate how the tools provided in PySAP can be beneficial.
Organization: K. Augustson