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 76–100 of 597 papers

TitleStatusHype
Adaptive-Rate Compressive Sensing Using Side Information—0
Ambient Occlusion via Compressive Visibility Estimation—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization—0
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method—0
Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications—0
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems—0
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
C^2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction—0
A Compressive Sensing Approach to Community Detection with Applications—0
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing—0
Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images—0
Comparison of threshold-based algorithms for sparse signal recovery—0
Compressed-Domain Detection and Estimation for Colocated MIMO Radar—0
Compressed Domain Image Classification Using a Dynamic-Rate Neural Network—0
Compressed domain vibration detection and classification for distributed acoustic sensing—0
Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces—0
Compressed sensing MRI using masked DCT and DFT measurements—0
Compressive Acquisition of Dynamic Scenes—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