Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR).
First, we estimate the blur kernel by computing the kernel coefficients with minimum total generalized variation that blur a downsampled version of the PAN image to approximate a linear combination of the LRMS image channels.
Common techniques that attempt to resolve the antenna ambiguity generally assume an unknown gain and phase error afflicting the radar measurements.
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications.
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems.
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements.
The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects.
An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.