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 481–490 of 597 papers

TitleStatusHype
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
A Compressive Sensing Approach to Community Detection with Applications—0
A Compressive Sensing Based Method for Harmonic State Estimation—0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data—0
Active Search for Sparse Signals with Region Sensing—0
Active User Detection of Uplink Grant-Free SCMA in Frequency Selective Channel—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