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Abstract #1191

Automatic determination of the regularization weighting for low rank reconstruction problems

Gabriel Varela-Mattatall1,2, Corey A Baron1,2, and Ravi S Menon1,2
1Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada, 2Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

Low rank is an appealing method to reconstruct multiple images that share common properties between them. The highest variance, from a singular value decomposition perspective, comes from acquisition noise; therefore, noise can be tracked and discarded by selecting either the ideal rank or denoising threshold. However, the a priori determination of either of them is still an open question. In this work, we develop a general, non-iterative, fast, and automatic procedure to determine the regularization weighting for low rank reconstruction problems.

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