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 61–70 of 597 papers

TitleStatusHype
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI—0
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing—0
Analyzing the group sparsity based on the rank minimization methods—0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
Block based Adaptive Compressive Sensing with Sampling Rate Control—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—0
Block-wise Lensless Compressive Camera—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
A data-driven approach to sampling matrix selection for 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