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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