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 171–180 of 597 papers

TitleStatusHype
Analyzing the group sparsity based on the rank minimization methods—0
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing—0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
Comparison of threshold-based algorithms for sparse signal recovery—0
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing—0
Comparative Study on Millimeter Wave Location-Based Beamforming—0
A data-driven approach to sampling matrix selection for compressive sensing—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