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Found 12 papers, 0 papers with code
Learning Model-Based Sparsity via Projected Gradient Descent
As an example we elaborate on application of the main results to estimation in Generalized Linear Model.
Representation and Coding of Signal Geometry
In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products.
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras.
A Recursive Born Approach to Nonlinear Inverse Scattering
The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects.
Compressive Imaging with Iterative Forward Models
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements.
SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
Online Convolutional Dictionary Learning for Multimodal Imaging
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems.
Accelerated Image Reconstruction for Nonlinear Diffractive Imaging
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications.
Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging
Common techniques that attempt to resolve the antenna ambiguity generally assume an unknown gain and phase error afflicting the radar measurements.
High-Contrast Reflection Tomography with Total-Variation Constraints
Inverse scattering is the process of estimating the spatial distribution of the scattering potential of an object by measuring the scattered wavefields around it.
Multiview Sensing With Unknown Permutations: An Optimal Transport Approach
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.
Fast and High-Quality Blind Multi-Spectral Image Pansharpening
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.
