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 221–230 of 597 papers

TitleStatusHype
Generalization Bounds for Sparse Random Feature ExpansionsCode0
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive SensingCode1
OpenICS: Open Image Compressive Sensing Toolbox and BenchmarkCode1
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI FeedbackCode1
Measuring Robustness in Deep Learning Based Compressive SensingCode1
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
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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