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 51–100 of 597 papers

TitleStatusHype
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Modeling and Optimization for Flexible Cylindrical Arrays-Enabled Wireless CommunicationsCode0
Multilinear Compressive Learning with Prior KnowledgeCode0
Multi-Scale Deep Compressive Sensing NetworkCode0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
An Efficient Algorithm for Clustered Multi-Task Compressive SensingCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam AlignmentCode0
Perceptual Compressive SensingCode0
Multi-Channel Deep Networks for Block-Based Image Compressive SensingCode0
Recursions Are All You Need: Towards Efficient Deep Unfolding NetworksCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal RecoveryCode0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
Learning to compress and search visual data in large-scale systemsCode0
Interpretable Recurrent Neural Networks Using Sequential Sparse RecoveryCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
Learning to Invert: Signal Recovery via Deep Convolutional NetworksCode0
Group-based Sparse Representation for Image RestorationCode0
Full Image Recover for Block-Based Compressive SensingCode0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and PerformanceCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
HUNet: Homotopy Unfolding Network for Image Compressive SensingCode0
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath ChannelsCode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
Adaptive Measurement Network for CS Image ReconstructionCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Chasing Better Deep Image Priors between Over- and Under-parameterizationCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Deep Regularized Compound Gaussian Network for Solving Linear Inverse ProblemsCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing ReconstructionCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Difference of Convolution for Deep Compressive SensingCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
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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