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 Sensing of Sparse Tensors—0
How to find real-world applications for compressive sensing—0
Compressive adaptive computational ghost imaging—0
Compressive Shift Retrieval—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals—0
Adaptive Temporal Compressive Sensing for Video—0
Adaptive low rank and sparse decomposition of video using compressive sensing—0
Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels—0
Feature-aware Label Space Dimension Reduction for Multi-label Classification—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