SOTAVerified

Compressive Sensing

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

Papers

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Showing 226–250 of 597 papers

TitleStatusHype
Weighed l1 on the simplex: Compressive sensing meets locality—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Compressive lensless endoscopy with partial speckle scanning—0
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System—0
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems—0
Distributed Video Adaptive Block Compressive Sensing—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton—0
Improved Coherence Index-Based Bound in Compressive Sensing—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
Faster Maximum Feasible Subsystem Solutions for Dense Constraint Matrices—0
Study on Compressed Sensing of Action Potential—0
Scalable Deep Compressive Sensing—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
On the Fourier transform of a quantitative trait: Implications for compressive sensing—0
Understanding Adversarial Attacks on Autoencoders—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond—0
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Compression Boosts Differentially Private Federated Learning—0
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All datasetsSet11 cs=50%BSD68 CS=50%BSDS100 - 2x upscalingUrban100 - 2x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMP-DUN-Plus (4-step)Average PSNR42.82—Unverified
2AMPA-NetAverage PSNR40.32—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.86—Unverified