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
Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware—0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—0
Reconstruction-free action inference from compressive imagers—0
Reconstruction-free Cascaded Adaptive Compressive Sensing—0
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging—0
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)—0
Recovering compressed images for automatic crack segmentation using generative models—0
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm—0
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Regional Total Electron Content Map Generation based on Compressive Sensing—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