Adversarially learned iterative reconstruction for imaging inverse problems

Understand

In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning.

• Therefore, it is imperative to develop unsupervised learning protocols that are competitive with supervised approaches in performance.
• Motivated by the maximum-likelihood principle, we propose an unsupervised learning framework for solving ill-posed inverse problems.
• Instead of seeking pixel-wise proximity between the reconstructed and the ground-truth images, the proposed approach learns an iterative reconstruction network whose output matches the ground-truth in distribution.