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 311–320 of 597 papers

TitleStatusHype
Convex Reconstruction of Structured Matrix Signals from Linear Measurements (I): Theoretical Results—0
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial NetworksCode0
Lipschitz Learning for Signal Recovery—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Difference of Convolution for Deep Compressive SensingCode0
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
Phase Retrieval using Untrained Neural Network Priors—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
A Hybrid Architecture for On-Device Compressive Machine 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