Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Non-negatively constrained image deblurring with an inexact interior point method
Journal of Computational and Applied Mathematics
Restoration of images based on subspace optimization accelerating augmented Lagrangian approach
Journal of Computational and Applied Mathematics
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In most cases astronomical images contain objects with very different intensities such as bright stars combined with faint nebulae. Since the noise is mainly due to photon counting (Poisson noise), the signal-to-noise ratio may be very different in different regions of the image. Moreover, the bright and faint objects have, in general, different angular scales. These features imply that the iterative methods which are most frequently used for the reconstruction of astronomical images, namely the Richardson-Lucy Method (RLM), also known in tomography as Expectation Maximization (EM) method, and the Iterative Space Reconstruction Algorithm (ISRA) do not work well in these cases. Also standard regularization approaches do not provide satisfactory results since a kind of adaptive regularization is required, in the sense that one needs a different regularization for bright and faint objects. In this paper we analyze a number of regularization functionals with this particular kind of adaptivity and we propose a simple modification of RLM and ISRA which takes into account these regularization terms. The preliminary results on a test object are promising.