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 581–590 of 597 papers

TitleStatusHype
Compressive neural representation of sparse, high-dimensional probabilities—0
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem—0
Compressive Sensing MRI with Wavelet Tree Sparsity—0
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization—0
Forest Sparsity for Multi-channel Compressive Sensing—0
Stable and robust sampling strategies for compressive imaging—0
Robust Dequantized Compressive Sensing—0
Compressive Acquisition of Dynamic Scenes—0
Sparse Estimation with Structured Dictionaries—0
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements—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