Mapped Variably Scaled Kernels: Applications to Solar Imaging

Marchetti, Francesco; Perracchione, Emma; Volpara, Anna; Massone, Anna Maria; Marchi, Stefano De; Piana, Michele

doi:10.1007/978-3-031-37117-2_39

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-37117-2_39

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Mapped Variably Scaled Kernels: Applications to Solar Imaging

Francesco Marchetti

Emma Perracchione

Anna Volpara

et al.

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2024

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Multi-scale CLEAN for Fourier-based hard x-ray solar imaging

Volpara,

Catalano,

Piana

et al. 2024

Inverse Problems

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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-based astronomical 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 flaring topographies.

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Multi-scale CLEAN for Fourier-based hard x-ray solar imaging

Volpara,

Catalano,

Piana

et al. 2024

Inverse Problems

View full text Add to dashboard Cite

show abstract

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Mapped Variably Scaled Kernels: Applications to Solar Imaging

Cited by 1 publication

References 25 publications

Multi-scale CLEAN for Fourier-based hard x-ray solar imaging

Multi-scale CLEAN for Fourier-based hard x-ray solar imaging

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