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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 231–240 of 597 papers

TitleStatusHype
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
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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