Self-Supervised Deep Equilibrium Models for Inverse Problems with
Theoretical Guarantees
- URL: http://arxiv.org/abs/2210.03837v1
- Date: Fri, 7 Oct 2022 22:19:26 GMT
- Title: Self-Supervised Deep Equilibrium Models for Inverse Problems with
Theoretical Guarantees
- Authors: Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang, Cihat Eldeniz,
Yuyang Hu, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
- Abstract summary: DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with deep unfolding (DU)
We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements.
- Score: 12.724731147883316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep
unfolding (DU) for image reconstruction. DEQ models-implicit neural networks
with effectively infinite number of layers-were shown to achieve
state-of-the-art image reconstruction without the memory complexity associated
with DU. While the performance of DEQ has been widely investigated, the
existing work has primarily focused on the settings where groundtruth data is
available for training. We present self-supervised deep equilibrium model
(SelfDEQ) as the first self-supervised reconstruction framework for training
model-based implicit networks from undersampled and noisy MRI measurements. Our
theoretical results show that SelfDEQ can compensate for unbalanced sampling
across multiple acquisitions and match the performance of fully supervised DEQ.
Our numerical results on in-vivo MRI data show that SelfDEQ leads to
state-of-the-art performance using only undersampled and noisy training data.
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