Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data

Jonathan I. Tamir, Stella X. Yu, Michael Lustig

Keywords: Unsupervised learning, inverse problems, model-based deep learning, computational imaging, magnetic resonance imaging

TL;DR: We present an unsupervised deep learning reconstruction for imaging inverse problems that combines neural networks with model-based constraints.

Abstract: Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regularization. Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data. During training, we use alternating minimization across an unrolled iterative network and jointly solve for the neural network weights and training set image reconstructions. At inference, we fix the weights and pass the measurements through the network. We compare reconstruction performance between unsupervised and supervised (i.e. with ground-truth) methods. We hypothesize this technique could be used to learn reconstruction when ground-truth data are unavailable, such as in high-resolution dynamic MRI.