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 411–420 of 597 papers

TitleStatusHype
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing—0
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning—0
Single pixel imaging at high pixel resolutions—0
Site-specific online compressive beam codebook learning in mmWave vehicular communication—0
Sketching for Large-Scale Learning of Mixture Models—0
SnapCap: Efficient Snapshot Compressive Video Captioning—0
Snapshot fiber spectral imaging using speckle correlations and compressive sensing—0
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas—0
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—0
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior—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