ML-Augmented Reconstruction
Cross-source consensus on ML-Augmented Reconstruction from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Benefits
Comparisons
Highlighted claims
- The proposed method accelerates reconstruction by inserting a learned fast-forward operator into a conventional iterative workflow. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- The method augments CDTools instead of replacing the ptychographic reconstruction framework. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- The learned prediction reinitializes the iterative solver before ordinary physics-based updates continue. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
- Because the learned operator acts on reconstructed objects, it is agnostic to detector size, pixel sampling, and solver details. — Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction