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
Full Image Recover for Block-Based Compressive SensingCode0
An Efficient Algorithm for Clustered Multi-Task Compressive SensingCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Group-based Sparse Representation for Image RestorationCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
Interpretable Recurrent Neural Networks Using Sequential Sparse RecoveryCode0
HUNet: Homotopy Unfolding Network for Image Compressive SensingCode0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal RecoveryCode0
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Learning to compress and search visual data in large-scale systemsCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Adaptive Measurement Network for CS Image ReconstructionCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive ImagingCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
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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