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 531–540 of 597 papers

TitleStatusHype
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor CompletionCode0
Graph-based Semi-supervised Local Clustering with Few Labeled NodesCode0
Sparse Bayesian Generative Modeling for Compressive SensingCode0
Sparse Depth Sensing for Resource-Constrained RobotsCode0
Towards Real-time Video Compressive Sensing on Mobile DevicesCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
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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