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 471–480 of 597 papers

TitleStatusHype
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction—0
User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access—0
Video Compressive Sensing for Dynamic MRI—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
Weighed l1 on the simplex: Compressive sensing meets locality—0
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—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