Keywords: AI/ML Image Reconstruction, Image Reconstruction, Accelerated imaging, compressed sensing, unsupervised learning
Motivation: Alternative unsupervised training methods are needed for training physics-driven deep learning reconstruction without fully-sampled data.
Goal(s): We propose a novel loss formulation, inspired by compressibility, to evaluate reconstruction quality in supervised, unsupervised and zero-shot settings.
Approach: We leverage reweighted $$$\ell_1$$$-norm, which corresponds to $$$\ell_0$$$-norm of a sparse signal, to evaluate reconstruction quality. In supervised setting, reference weights are used for reweighting, while in unsupervised case, they are updated after each reweighting.
Results: Our findings demonstrate that the networks trained with this loss outperform conventional compressed sensing, while performing similarly to deep learning methods trained using established supervised and unsupervised techniques.
Impact: This work proposes an alternative compressibility-inspired loss formulation that is applicable to supervised, unsupervised and zero-shot learning problems for the training of physics-driven reconstruction neural networks. This approach utilizes compressibility and convexity for learning.
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