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 501–510 of 597 papers

TitleStatusHype
Structured Sparsity: Discrete and Convex approaches—0
Ambient Occlusion via Compressive Visibility Estimation—0
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity—0
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms—0
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor CompletionCode0
Blind Compressive Sensing Framework for Collaborative Filtering—0
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared—0
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing—0
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals—0
Compressed sensing MRI using masked DCT and DFT measurements—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