Stochastic Primal-Dual Deep Unrolling Networks for Imaging Inverse Problems

In this work we propose a new type of efficient deep-unrolling networks for solving imaging inverse problems. Classical deep-unrolling methods require full forward operator and its adjoint across each layer, and hence can be computationally more expensive than other end-to-end methods such as FBP-ConvNet, especially in 3D image reconstruction tasks. We propose a stochastic (ordered-subsets) extension of the Learned Primal-Dual (LPD) which is a state-of-the-art unrolling network. In our unrolling network, we only use subsets of the forward and adjoint operator, to achieve computational efficiency. We provide theoretical analysis of a special case within our LSPD framework, suggesting that our LSPD network has the potential to achieve the same accuracy of full batch LPD network with only accessing the subsets of operators. Our numerical results demonstrate the effectiveness of our approach in X-ray CT imaging task, showing that our networks achieve similar reconstruction accuracies as the full-batch LPD, while requiring only a fraction of the computation.