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 511–520 of 597 papers

TitleStatusHype
LiSens --- A Scalable Architecture for Video Compressive Sensing—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting—0
Compressive Hyperspectral Imaging with Side Information—0
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images—0
Limits on Support Recovery with Probabilistic Models: An Information-Theoretic Framework—0
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries—0
Reconstruction-free action inference from compressive imagers—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Efficient Sampling for Learning Sparse Additive Models in High Dimensions—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