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 141–150 of 597 papers

TitleStatusHype
Adaptive foveated single-pixel imaging with dynamic super-sampling—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
Compressive Sensing via Low-Rank Gaussian Mixture Models—0
Compressive Sensing with Tensorized Autoencoder—0
Compressive Sensing via Convolutional Factor Analysis—0
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Compressive Shift Retrieval—0
Compressive Single-pixel Fourier Transform Imaging using Structured Illumination—0
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms—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