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 51–75 of 597 papers

TitleStatusHype
An Efficient Method for Robust Projection Matrix DesignCode0
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
An Efficient Algorithm for Clustered Multi-Task Compressive SensingCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and PerformanceCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Adaptive Measurement Network for CS Image ReconstructionCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Difference of Convolution for Deep Compressive SensingCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
Deep Regularized Compound Gaussian Network for Solving Linear Inverse ProblemsCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
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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