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 491–500 of 597 papers

TitleStatusHype
Adaptive and Cascaded Compressive Sensing—0
Adaptive foveated single-pixel imaging with dynamic super-sampling—0
Adaptive low rank and sparse decomposition of video using compressive sensing—0
Adaptive-Rate Compressive Sensing Using Side Information—0
Adaptive Temporal Compressive Sensing for Video—0
A data-driven approach to sampling matrix selection for compressive sensing—0
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing—0
A Deep Learning Approach to Structured Signal Recovery—0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing—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