Abstract
In this paper, we propose a variational model to restore images degraded by blur and Rician noise. This model uses total variation regularization with a fidelity term involving the Rician probability distribution. For its numerical solution, we apply and compare the L 2 and Sobolev (H 1) gradient descents, and the iterative method called split Bregman (with a convexified fidelity term). Numerical results are shown on synthetic magnetic resonance imaging (MRI) data corrupted with Rician noise and Gaussian blur, both with known standard deviations.Theoretical analysis of the proposed model is briefly discussed.
Work supported by National Science Foundation grants DMS-0714945, CCF/ITR Expeditions 0926127 (UCLA Center for Domain-Specific Computing), and by a NSF postdoctoral fellowship.
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Getreuer, P., Tong, M., Vese, L.A. (2011). A Variational Model for the Restoration of MR Images Corrupted by Blur and Rician Noise. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_63
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DOI: https://doi.org/10.1007/978-3-642-24028-7_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
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