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

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Showing 176–200 of 597 papers

TitleStatusHype
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—0
Analyzing the group sparsity based on the rank minimization methods—0
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing—0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
Comparison of threshold-based algorithms for sparse signal recovery—0
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing—0
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection—0
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing—0
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective—0
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery—0
Comparative Study on Millimeter Wave Location-Based Beamforming—0
A data-driven approach to sampling matrix selection for compressive sensing—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Deep De-Aliasing for Fast Compressive Sensing MRI—0
Designed Measurements for Vector Count Data—0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—0
Dictionary-Learning-Based Reconstruction Method for Electron Tomography—0
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Adaptive Temporal Compressive Sensing for Video—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