In this work, the feasibility of employing denoising to recover MR images from undersampled data is demonstrated. By embedding denoisers into the Laplacian-based regularization functional and solving the resulting optimization problem, state-of-the-art results are achieved. Performance of several denoisers and compressed sensing methods is compared in four cardiac MRI datasets. We show that denoising-based reconstruction can outperform soft-thresholding-based algorithms in terms of normalized MSE.
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