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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 501–550 of 597 papers

TitleStatusHype
Regularizing linear inverse problems with convolutional neural networks—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Remote Multilinear Compressive Learning with Adaptive Compression—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Compressive Sensing with Wigner D-functions on Subsets of the Sphere—0
Restricted Structural Random Matrix for Compressive Sensing—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
Review of Algorithms for Compressive Sensing of Images—0
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity—0
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors—0
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals—0
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit—0
Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences—0
Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints—0
Robust Dequantized Compressive Sensing—0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Recursions Are All You Need: Towards Efficient Deep Unfolding NetworksCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
An Efficient Method for Robust Projection Matrix DesignCode0
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable GuaranteesCode0
Perceptual Compressive SensingCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
HUNet: Homotopy Unfolding Network for Image Compressive SensingCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
Training Image Estimators without Image Ground TruthCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath ChannelsCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative ModelsCode0
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
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative PriorsCode0
Provable Dynamic Robust PCA or Robust Subspace TrackingCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam AlignmentCode0
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Interpretable Recurrent Neural Networks Using Sequential Sparse RecoveryCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
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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