2023/03/28 by Volpara, Anna, Piana, Michele, Massone, Anna Maria
#45Q05 #47A52 #68U10 #8508 #FOS: Mathematics #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Numerical Analysis (math.NA) #Solar and Stellar Astrophysics (astro-ph.SR)
paper · doi:10.48550/arxiv.2303.16272
Multi-scale deconvolution is an ill-posed inverse problem in imaging, with applications ranging from microscopy, through medical imaging, to astronomical remote sensing. In the case of high-energy space telescopes, multi-scale deconvolution algorithms need to account for the peculiar property of native measurements, which are sparse samples of the Fourier transform of the incoming radiation. The present paper proposes a multi-scale version of CLEAN, which is the most popular iterative deconvolution method in Fourier space imaging. Using synthetic data generated according to a simulated but realistic source configuration, we show that this multi-scale version of CLEAN performs better than the original one in terms of accuracy, photometry, and regularization. Further, the application to a data set measured by the NASA Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) shows the ability of multi-scale CLEAN to reconstruct rather complex topographies, characteristic of a real flaring event.