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 361–370 of 597 papers

TitleStatusHype
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Fingerprint Recognition under Missing Image Pixels Scenario—0
Face Recognition using Compressive Sensing—0
Compressed Domain Image Classification Using a Dynamic-Rate Neural Network—0
Learning to compress and search visual data in large-scale systemsCode0
Joint group and residual sparse coding for image compressive sensing—0
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks—0
Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach—0
Multi-Scale Deep Compressive Sensing NetworkCode0
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method—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