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 126–150 of 597 papers

TitleStatusHype
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Block-wise Lensless Compressive Camera—0
A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—0
Block based Adaptive Compressive Sensing with Sampling Rate Control—0
Adaptive low rank and sparse decomposition of video using compressive sensing—0
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint—0
Blind Orthogonal Least Squares based Compressive Spectrum Sensing—0
Blind Compressive Sensing Framework for Collaborative Filtering—0
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems—0
Biomedical Signals Reconstruction Under the Compressive Sensing Approach—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks—0
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover—0
Adaptive foveated single-pixel imaging with dynamic super-sampling—0
A Probabilistic Bayesian Approach to Recover R_2^* map and Phase Images for Quantitative Susceptibility Mapping—0
Compressive Sensing via Low-Rank Gaussian Mixture Models—0
Compressive Sensing with Tensorized Autoencoder—0
Compressive Sensing via Convolutional Factor Analysis—0
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Compressive Shift Retrieval—0
Compressive Single-pixel Fourier Transform Imaging using Structured Illumination—0
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms—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