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 371–380 of 597 papers

TitleStatusHype
Image Restoration from Patch-based Compressed Sensing Measurement—0
Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters—0
Improved Coherence Index-Based Bound in Compressive Sensing—0
Information-Theoretic Bounds for Adaptive Sparse Recovery—0
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models—0
In-sector Compressive Beam Alignment for MmWave and THz Radios—0
Instance Optimal Decoding and the Restricted Isometry Property—0
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation—0
IoT Connectivity Technologies and Applications: A Survey—0
ISAR imaging of space objects using encoded apertures—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