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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 351–400 of 597 papers

TitleStatusHype
PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Networks Reconstruction—0
Parameterless Optimal Approximate Message Passing—0
Performance Indicator in Multilinear Compressive Learning—0
Phase Retrieval using Untrained Neural Network Priors—0
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms—0
PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network—0
PLUGIn-CS: A simple algorithm for compressive sensing with generative prior—0
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction—0
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis—0
Prior Information-Aided ADMM for Multi-User Detection in Codebook-Based Grant-Free NOMA: Dynamic Scenarios—0
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Provable Convergence of Plug-and-Play Priors with MMSE denoisers—0
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks—0
Quantifying the influence of conformational uncertainty in biomolecular solvation—0
Quantity over Quality: Dithered Quantization for Compressive Radar Systems—0
RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation—0
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method—0
Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware—0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—0
Reconstruction-free action inference from compressive imagers—0
Reconstruction-free Cascaded Adaptive Compressive Sensing—0
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging—0
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)—0
Recovering compressed images for automatic crack segmentation using generative models—0
Recovery of Images with Missing Pixels using a Gradient Compressive Sensing Algorithm—0
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder—0
Regional Total Electron Content Map Generation based on Compressive Sensing—0
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
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover—0
Robust Mean Estimation in High Dimensions via _0 Minimization—0
Sample Complexity Lower Bounds for Compressive Sensing with Generative Models—0
Sampling and Reconstruction of Sparse Signals in Shift-Invariant Spaces: Generalized Shannon's Theorem Meets Compressive Sensing—0
Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing—0
Scalable Deep Compressive Sensing—0
Secure Directional Modulation with Movable Antenna Array Aided by RIS—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