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 301–310 of 597 papers

TitleStatusHype
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Line-based compressive sensing for low-power visual applications—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—0
Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware—0
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems—0
Training Image Estimators without Image Ground TruthCode0
Error Resilient Deep Compressive Sensing—0
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation—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