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 131–140 of 597 papers

TitleStatusHype
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Compressive phase-only filtering at extreme compression rates—0
A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC—0
A fast patch-dictionary method for whole image recovery—0
Active Search for Sparse Signals with Region Sensing—0
Compressive Pattern Matching on Multispectral Data—0
Compressive neural representation of sparse, high-dimensional probabilities—0
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach—0
Compressively Sensed Image Recognition—0
A Parallel Compressive Imaging Architecture for One-Shot Acquisition—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