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 101–125 of 597 papers

TitleStatusHype
Compression Boosts Differentially Private Federated Learning—0
Compression Ratio Learning and Semantic Communications for Video Imaging—0
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging—0
Compressive Acquisition of Dynamic Scenes—0
A Compressive Sensing Approach to Community Detection with Applications—0
Compressive adaptive computational ghost imaging—0
Compressive dual-comb spectroscopy—0
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions—0
Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera—0
Compressive hyperspectral imaging via adaptive sampling and dictionary learning—0
Compressive Hyperspectral Imaging with Side Information—0
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography—0
Compressive lensless endoscopy with partial speckle scanning—0
Compressive Light Field Reconstructions using Deep Learning—0
Compressively Sensed Image Recognition—0
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach—0
Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO—0
Compressive Pattern Matching on Multispectral Data—0
Compressive phase-only filtering at extreme compression rates—0
Compressive Phase Retrieval: Optimal Sample Complexity with Deep Generative Priors—0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections—0
Compressive Scanning Transmission Electron Microscopy—0
Compressive sensing adaptation for polynomial chaos expansions—0
Compressive sensing based privacy for fall detection—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