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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 401–450 of 597 papers

TitleStatusHype
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration—0
Instance Optimal Decoding and the Restricted Isometry Property—0
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable GuaranteesCode0
Comparison of threshold-based algorithms for sparse signal recovery—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Light Field Reconstructions using Deep Learning—0
Perceptual Compressive SensingCode0
Full Image Recover for Block-Based Compressive SensingCode0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—0
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning—0
Binary Compressive Sensing via Smoothed _0 Gradient Descent—0
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms—0
Compressive sensing adaptation for polynomial chaos expansions—0
Nearly Optimal Robust Subspace TrackingCode1
Tomographic Reconstruction using Global Statistical Prior—0
Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary—0
Fully Convolutional Measurement Network for Compressive Sensing Image ReconstructionCode0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis—0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging—0
Adaptive Measurement Network for CS Image ReconstructionCode0
Optimized Structured Sparse Sensing Matrices for Compressive Sensing—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
A Compressive Sensing Approach to Community Detection with Applications—0
Bayesian Compressive Sensing Using Normal Product Priors—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices—0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—0
Experimental comparison of single-pixel imaging algorithms—0
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring—0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
Two-pixel polarimetric camera by compressive sensing—0
Measurement-Adaptive Sparse Image Sampling and Recovery—0
Nonconvex penalties with analytical solutions for one-bit compressive sensing—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Restoration from Patch-based Compressed Sensing Measurement—0
Towards Understanding the Invertibility of Convolutional Neural Networks—0
Provable Dynamic Robust PCA or Robust Subspace TrackingCode0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
Deep De-Aliasing for Fast Compressive Sensing MRI—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences—0
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration—0
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization—0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
Multilinear compressive sensing and an application to convolutional linear networks—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—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