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 261–270 of 597 papers

TitleStatusHype
Regional Total Electron Content Map Generation based on Compressive Sensing—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Robust Mean Estimation in High Dimensions via _0 Minimization—0
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing—0
Optimal Data Detection and Signal Estimation in Systems with Input Noise—0
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access—0
Fast Nonconvex T_2^* Mapping Using ADMM—0
Third-Order Statistics Reconstruction from Compressive Measurements—0
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam AlignmentCode0
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data—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