Statistical Analysis for Reconstruction of Tomographic Solar Images Using Compressive Sensing

Tomography of Solar Corona is a technique that allows the re-construction its physical parameters but it has a limited image quality due to its nature. Compressive Sensing (CS) is a technique that allows to a signal having a sparse representation to be reconstructed taken from a nonsparse representation. In a previously work, we evaluated the performance of CS based on the preprocessing of the available k-space samples, using images from SoHO mission, that have led to the im-provement of the quality images, exploring their possibility in the application of Solar Corona analysis. For this article, we use a bank of images LASCO C2 (31 images) which analyzes them statistically to determine the signal distribution and the equality of matched pairs of ob-servations. With this analysis was possible to determine the optimal number of projections used, 250, varying the number of angles considered (from 40 to 1000).