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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
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint—0
MC-ISTA-Net: Adaptive Measurement and Initialization and Channel Attention Optimization inspired Neural Network for Compressive Sensing—0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Spatial Channel Covariance Estimation for Hybrid Architectures Based on Tensor Decompositions—0
Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization—0
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing—0
Object tracking in video signals using Compressive Sensing—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Fingerprint Recognition under Missing Image Pixels Scenario—0
Face Recognition using Compressive Sensing—0
Compressed Domain Image Classification Using a Dynamic-Rate Neural Network—0
Learning to compress and search visual data in large-scale systemsCode0
Joint group and residual sparse coding for image compressive sensing—0
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks—0
Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach—0
Multi-Scale Deep Compressive Sensing NetworkCode0
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method—0
Quantity over Quality: Dithered Quantization for Compressive Radar Systems—0
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics—0
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach—0
Compressive Sensing and Morphology Singular Entropy-Based Real-time Secondary Voltage Control of Multi-area Power Systems—0
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI—0
Forensic Discrimination between Traditional and Compressive Imaging Systems—0
Non-Local Compressive Sensing Based SAR Tomography—0
Compressive Single-pixel Fourier Transform Imaging using Structured Illumination—0
Snapshot fiber spectral imaging using speckle correlations and compressive sensing—0
Compressively Sensed Image Recognition—0
A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC—0
Multi-Scale Deep Compressive Sensing NetworkCode0
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties—0
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera—0
Joint optimization for compressive video sensing and reconstruction under hardware constraints—0
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and ThresholdsCode0
Comparative Study on Millimeter Wave Location-Based Beamforming—0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Experimental Results of a 3D Millimeter-Wave Compressive-Reflector-Antenna Imaging System—0
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing ReconstructionCode0
Convolutional sparse coding for capturing high speed video content—0
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior—0
Depth and Transient Imaging With Compressive SPAD Array Cameras—0
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI—0
Frequency-modulated continuous-wave LiDAR compressive depth-mapping—0
Active User Detection of Uplink Grant-Free SCMA in Frequency Selective Channel—0
Compressive Sensing Imaging of 3-D Object by a Holographic Algorithm—0
Estimation with Low-Rank Time-Frequency Synthesis Models—0
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
Kinetic 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