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 526–550 of 597 papers

TitleStatusHype
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