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 101–125 of 597 papers

TitleStatusHype
An Efficient Method for Robust Projection Matrix DesignCode0
Multilinear Compressive Learning with Prior KnowledgeCode0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
Compressive Closeness in NetworksCode0
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive SensingCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Perceptual Compressive SensingCode0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
Compressive Image Scanning MicroscopeCode0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
Deep Fully-Connected Networks for Video Compressive SensingCode0
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor CompletionCode0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Sparse Bayesian Generative Modeling for Compressive SensingCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive VideoCode0
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI—0
Compressive sensing adaptation for polynomial chaos expansions—0
Compressive Scanning Transmission Electron Microscopy—0
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
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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