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 with no code
On Generalization Bounds for Deep Compound Gaussian Neural Networks
In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior.
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission.
Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images
It is concluded that the gOMP algorithm reconstructs the hyperspectral images with higher accuracy as well as faster convergence when the pixels are highly sparsified and hence at the expense of reducing the quality of the recovered images with respect to the original images.
SnapCap: Efficient Snapshot Compressive Video Captioning
To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap.
MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)
We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA.
Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication
Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization.
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing
We aim to recover an $n \times q$ matrix, $\X^* =[ \x_1^*, \x_2^*, \cdots , \x_q^*]$ from $m$ independent linear projections of each of its $q$ columns, given by $\y_k :=\A_k\x_k^*$, $k \in [q]$.
PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network
Compressive sensing (CS) has been widely applied in signal and image processing fields.
Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning
Underwater Sound Speed Profile (SSP) distribution has great influence on the propagation mode of acoustic signal, thus the fast and accurate estimation of SSP is of great importance in building underwater observation systems.
Underwater Sound Speed Profile Construction: A Review
The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method.
