Search Results for author:
Found 2 papers, 2 papers with code
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.
Learned Interferometric Imaging for the SPIDER Instrument
These approaches use deep learning to learn prior information from training data, increasing the reconstruction quality, and significantly reducing the computation time required to recover images by orders of magnitude.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
