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 551–560 of 597 papers

TitleStatusHype
Video Compressive Sensing for Dynamic MRI—0
Multiscale Shrinkage and Lévy Processes—0
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing—0
Adaptive-Rate Compressive Sensing Using Side Information—0
Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions—0
New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications—0
Designed Measurements for Vector Count Data—0
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization—0
Dictionary-Learning-Based Reconstruction Method for Electron Tomography—0
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach—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