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 351–375 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
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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