STATISTICAL ANALYSIS FOR RECONSTRUCTION OF TOMOGRAPHIC SOLAR IMAGES USING COMPRESSIVE SENSING

Tomography of Solar Corona is a technique that allows the reconstruction 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 improvement 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 observations. With this analysis was possible to determine the optimal number of projections used, 250, varying the number of angles considered (from 40 to 1000).