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 261–270 of 597 papers

TitleStatusHype
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach—0
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A Compressive Sensing Approach to Community Detection with Applications—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing—0
Convolutional sparse coding for capturing high speed video content—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