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 491–500 of 597 papers

TitleStatusHype
Frequency-Based Environment Matting by Compressive Sensing—0
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing—0
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
Quantifying the influence of conformational uncertainty in biomolecular solvation—0
Compressive Sensing via Low-Rank Gaussian Mixture Models—0
Distributed Compressive Sensing: A Deep Learning Approach—0
A Deep Learning Approach to Structured Signal Recovery—0
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing—0
Toward a Robust Sparse Data Representation for Wireless Sensor Networks—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