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Found 5 papers, 4 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.
On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector
In photoacoustic tomography (PAT) with flat sensor, we routinely encounter two types of limited data.
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain
We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame.
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming.
