We consider two algorithmic challenges for the compressed sensing MRI community: (1) the difficulty of tuning free model parameters and (2) the need to converge quickly. The authors have developed a parameter-free approach to reconstruction which accommodates structurally rich regularizers that can be automatically adapted to near-optimality, removing the need for manual adjustment between images or sampling schemes. We evaluate the algorithm’s performance on three test images of varying type and dimension and find that it converges faster and to a lower mean-squared error than its competitors, even when they are optimally tuned.
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