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 551–597 of 597 papers

TitleStatusHype
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
Modeling and Optimization for Flexible Cylindrical Arrays-Enabled Wireless CommunicationsCode0
Deep Regularized Compound Gaussian Network for Solving Linear Inverse ProblemsCode0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk EstimateCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Adaptive Measurement Network for CS Image ReconstructionCode0
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and ThresholdsCode0
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and PerformanceCode0
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive SensingCode0
Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet FramesCode0
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse ProblemsCode0
Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial NetworksCode0
Multi-Channel Deep Networks for Block-Based Image Compressive SensingCode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
Full Image Recover for Block-Based Compressive SensingCode0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
Generalization Bounds for Sparse Random Feature ExpansionsCode0
Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal RecoveryCode0
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing ReconstructionCode0
Multilinear Compressive LearningCode0
Multilinear Compressive Learning with Prior KnowledgeCode0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive VideoCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Invert: Signal Recovery via Deep Convolutional NetworksCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Multi-Scale Deep Compressive Sensing NetworkCode0
Multi-Scale Deep Compressive Sensing NetworkCode0
Compressive Closeness in NetworksCode0
Operational Support Estimator NetworksCode0
Group-based Sparse Representation for Image RestorationCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative PriorsCode0
Training Image Estimators without Image Ground-TruthCode0
Compressive Image Scanning MicroscopeCode0
Chasing Better Deep Image Priors between Over- and Under-parameterizationCode0
Difference of Convolution for Deep Compressive SensingCode0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
An Efficient Algorithm for Clustered Multi-Task 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