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Found 7 papers, 1 papers with code
Learning In-between Imagery Dynamics via Physical Latent Spaces
We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps.
Leveraging joint sparsity in hierarchical Bayesian learning
We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors.
Sequential edge detection using joint hierarchical Bayesian learning
This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data.
Sub-aperture SAR Imaging with Uncertainty Quantification
As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient.
Stabilizing Radial Basis Function Methods for Conservation Laws Using Weakly Enforced Boundary Conditions
It is well understood that boundary conditions (BCs) may cause global radial basis function (RBF) methods to become unstable for hyperbolic conservation laws (CLs).
Numerical Analysis Numerical Analysis 35L65, 41A05, 41A30, , 65D05, 65M12
Estimation and uncertainty quantification for piecewise smooth signal recovery
This expands the class of problems available to Bayesian learning to include, e. g., inverse problems dealing with the recovery of piecewise smooth functions or signals from data.
Using Frame Theoretic Convolutional Gridding for Robust Synthetic Aperture Sonar Imaging
Recent progress in synthetic aperture sonar (SAS) technology and processing has led to significant advances in underwater imaging, outperforming previously common approaches in both accuracy and efficiency.
