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 381–390 of 597 papers

TitleStatusHype
Regularizing linear inverse problems with convolutional neural networks—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Remote Multilinear Compressive Learning with Adaptive Compression—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Compressive Sensing with Wigner D-functions on Subsets of the Sphere—0
Restricted Structural Random Matrix for Compressive Sensing—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
Review of Algorithms for Compressive Sensing of Images—0
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity—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