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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–325 of 597 papers

TitleStatusHype
Robust Symbol Detection in Overloaded NOMA Systems—0
Compressive sensing based privacy for fall detection—0
On Recoverability of Randomly Compressed Tensors with Low CP Rank—0
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
Convex Reconstruction of Structured Matrix Signals from Linear Measurements (I): Theoretical Results—0
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial NetworksCode0
Lipschitz Learning for Signal Recovery—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Difference of Convolution for Deep Compressive SensingCode0
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
A Hybrid Architecture for On-Device Compressive Machine Learning—0
Phase Retrieval using Untrained Neural Network Priors—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models—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