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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 451–500 of 597 papers

TitleStatusHype
Non-Local Compressive Sensing Based SAR Tomography—0
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Object tracking in video signals using Compressive Sensing—0
Off-grid Multi-Source Passive Localization Using a Moving Array—0
One-Bit Compressive Sensing: Can We Go Deep and Blind?—0
One-bit compressive sensing with norm estimation—0
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing—0
On Generalization Bounds for Deep Compound Gaussian Neural Networks—0
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics—0
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach—0
On reconstruction algorithms for signals sparse in Hermite and Fourier domains—0
On Recoverability of Randomly Compressed Tensors with Low CP Rank—0
Onsager-corrected deep learning for sparse linear inverse problems—0
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements—0
A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
Optimal Data Detection and Signal Estimation in Systems with Input Noise—0
Optimal Sensor Placement and Enhanced Sparsity for Classification—0
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding—0
Optimization of retina-like illumination patterns in ghost imaging—0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
Optimizing Binary Symptom Checkers via Approximate Message Passing—0
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
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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