Training Adaptive Reconstruction Networks for Blind Inverse Problems

Understand

Neural networks allow solving many ill-posed inverse problems with unprecedented performance.

• Physics informed approaches already progressively replace carefully hand-crafted reconstruction algorithms in real applications.
• However, these networks suffer from a major defect: when trained on a given forward operator, they do not generalize well to a different one.
• The aim of this paper is twofold.