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 451–460 of 597 papers

TitleStatusHype
Third-Order Statistics Reconstruction from Compressive Measurements—0
Tomographic Reconstruction using Global Statistical Prior—0
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier—0
Toward a Robust Sparse Data Representation for Wireless Sensor Networks—0
Towards Understanding the Invertibility of Convolutional Neural Networks—0
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets—0
Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems—0
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior—0
Tree-Structure Bayesian Compressive Sensing for Video—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—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