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 571–580 of 597 papers

TitleStatusHype
Compressive hyperspectral imaging via adaptive sampling and dictionary learning—0
Compressive Hyperspectral Imaging with Side Information—0
Compressive lensless endoscopy with partial speckle scanning—0
Compressive Light Field Reconstructions using Deep Learning—0
Compressively Sensed Image Recognition—0
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach—0
Compressive neural representation of sparse, high-dimensional probabilities—0
Compressive Pattern Matching on Multispectral Data—0
Compressive phase-only filtering at extreme compression rates—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—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