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

Showing 111120 of 597 papers

TitleStatusHype
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor CompletionCode0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Sparse Bayesian Generative Modeling for Compressive SensingCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMP-DUN-Plus (4-step)Average PSNR42.82Unverified
2AMPA-NetAverage PSNR40.32Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.95Unverified
#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR35.86Unverified