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 151–175 of 597 papers

TitleStatusHype
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data—0
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction—0
Compressive hyperspectral imaging via adaptive sampling and dictionary learning—0
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera—0
A Bayesian Lasso based Sparse Learning Model—0
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions—0
Compressive dual-comb spectroscopy—0
An Off-grid Compressive Sensing Strategy for the Subarray Synthesis of Non-uniform Linear Arrays—0
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing—0
Compressive adaptive computational ghost imaging—0
Compressive Acquisition of Dynamic Scenes—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
An Ensemble Approach for Compressive Sensing with Quantum—0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
Compression Boosts Differentially Private Federated Learning—0
Compressed Sensing SAR Imaging with Multilook Processing—0
Compressed sensing MRI using masked DCT and DFT measurements—0
A Deep Learning Approach to Structured Signal Recovery—0
Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces—0
Compressed domain vibration detection and classification for distributed acoustic sensing—0
Compressed Domain Image Classification Using a Dynamic-Rate Neural Network—0
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
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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