Primal-Dual Splitting Deep Unrolling for Image Reconstruction

Abstract

Image reconstruction, which recovers an original image from incomplete and noisy observed data, is inherently an ill-posed inverse problem. To overcome the ill-posedness, a model-based approach has enabled image reconstruction under complex physical constraints while maintaining mathematical rigor. However, the approach has practical limitations in the representation ability of hand-crafted regularization and parameter tuning. To address these challenges, this article provides an overview of deep unrolling, which constructs neural networks by unrolling an optimization algorithm. In particular, we review the fundamental concepts and recent trends in unrolling networks based on primal-dual splitting (PDS) type algorithms.