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.

翻译：多尺度反卷积是成像领域的一个不适定逆问题，其应用范围涵盖显微成像、医学成像以及天文遥感。在高能空间望远镜的情况下，多尺度反卷积算法需要考虑原始测量的特殊性质，即入射辐射傅里叶变换的稀疏采样。本文提出了CLEAN算法的多尺度版本，CLEAN是傅里叶空间成像中最流行的迭代反卷积方法。利用基于仿真但真实的源配置生成的合成数据，我们证明了该多尺度CLEAN版本在精度、测光性能和正则化效果方面均优于原始版本。此外，将该算法应用于NASA鲁文·拉马提高能太阳光谱成像仪（RHESSI）实测数据集，结果表明多尺度CLEAN能够重建真实耀斑事件特有的较为复杂的拓扑结构。