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
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
We develop a general, non-iterative, fast, and automatic procedure to determine the regularization weighting for low-rank reconstruction problems.
Impact of the size of the dataset for automatic low rank reconstructions using SNR 5 and an under-sampling factor of 4x. The first row corresponds to the mean appearance of the reconstructions, $$$\hat{X}$$$, for 5,10,40,100, and 600 images (from left to right, respectively). The second row corresponds to the absolute difference with respect to the reference, $$$X$$$, as $$$5 \times |\hat{X}-X|$$$.
Impact of the size of the dataset for automatic low rank reconstructions using SNR 30 and an under-sampling factor of 14x. The first row corresponds to the mean appearance of the reconstructions, $$$\hat{X}$$$, for 5,10,40,100, and 600 images (from left to right, respectively). The second row corresponds to the absolute difference with respect to the reference, $$$X$$$, as $$$5 \times |\hat{X}-X|$$$.