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.
In several applications, including imaging of deformable objects while in motion, simultaneous localization and mapping, and unlabeled sensing, we encounter the problem of recovering a signal that is measured subject to unknown permutations.
Inverse scattering is the process of estimating the spatial distribution of the scattering potential of an object by measuring the scattered wavefields around it.
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.
In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products.