Compressive Sensing
109 papers with code • 5 benchmarks • 4 datasets
Compressive Sensing is a new signal processing framework for efficiently acquiring and reconstructing a signal that have a sparse representation in a fixed linear basis.
Source: Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
Benchmarks
These leaderboards are used to track progress in Compressive Sensing
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Libraries
Use these libraries to find Compressive Sensing models and implementations
BSD
Urban100
Set5
Set11
Latest papers
Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems
Incorporating prior information into inverse problems, e. g. via maximum-a-posteriori estimation, is an important technique for facilitating robust inverse problem solutions.
An Efficient Algorithm for Clustered Multi-Task Compressive Sensing
al5250/multics
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This paper considers clustered multi-task compressive sensing, a hierarchical model that solves multiple compressive sensing tasks by finding clusters of tasks that leverage shared information to mutually improve signal reconstruction.
Compressive Image Scanning Microscope
We present a novel approach to implement compressive sensing in laser scanning microscopes (LSM), specifically in image scanning microscopy (ISM), using a single-photon avalanche diode (SPAD) array detector.
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing
Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance.
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems
clyons19/CG-Net
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IEEE Transactions on Signal Processing 2023
The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution.
Recursions Are All You Need: Towards Efficient Deep Unfolding Networks
rawwad-alhejaili/recursions-are-all-you-need
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Secondly, we randomize the number of recursions during training to decrease the overall training time.
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
songjiechong/octuf
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CVPR 2023
And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block.
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRI
This claim is based on comparisons on 8 different retrospectively under sampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo radial under sampling, at multiple sampling rates.
A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback
sharanmourya/pytorch_stnet
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Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming.
