Fast Equivariant Imaging: Acceleration for Unsupervised Learning via Augmented Lagrangian and Auxiliary PnP Denoisers

• URL: http://arxiv.org/abs/2507.06764v1
• Date: Wed, 09 Jul 2025 11:47:06 GMT
• Title: Fast Equivariant Imaging: Acceleration for Unsupervised Learning via Augmented Lagrangian and Auxiliary PnP Denoisers
• Authors: Guixian Xu, Jinglai Li, Junqi Tang,
• Abstract summary: We propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to efficiently train deep imaging networks without ground-magnitude data.<n>FEI shows superior efficiency and performance compared to vanilla Equivariant Imaging paradigm.
• Score: 4.287621751502392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to efficiently train deep imaging networks without ground-truth data. From the perspective of reformulating the Equivariant Imaging based optimization problem via the method of Lagrange multipliers and utilizing plug-and-play denoisers, this novel unsupervised scheme shows superior efficiency and performance compared to vanilla Equivariant Imaging paradigm. In particular, our PnP-FEI scheme achieves an order-of-magnitude (10x) acceleration over standard EI on training U-Net with CT100 dataset for X-ray CT reconstruction, with improved generalization performance.

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