In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and 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 the vanilla Equivariant Imaging paradigm. In particular, our FEI schemes achieve an order-of-magnitude (10x) acceleration over standard EI on training U-Net for X-ray CT reconstruction and image inpainting, with improved generalization performance.

翻译：本文提出快速等变成像（FEI），一种无需真实数据即可快速高效训练深度成像网络的新型无监督学习框架。通过拉格朗日乘子法重构等变成像优化问题，并采用即插即用去噪器，该无监督方案相较于原始等变成像范式展现出卓越的效率与性能。具体而言，在训练U-Net进行X射线CT重建和图像修复时，我们的FEI方案比标准EI实现了一个数量级（10倍）的加速，同时提升了泛化性能。