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 101–110 of 597 papers

TitleStatusHype
Compression Boosts Differentially Private Federated Learning—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
Compressive Acquisition of Dynamic Scenes—0
Compressive lensless endoscopy with partial speckle scanning—0
Compressive adaptive computational ghost imaging—0
Compressive dual-comb spectroscopy—0
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions—0
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera—0
Compressive Light Field Reconstructions using Deep Learning—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