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 191–200 of 597 papers

TitleStatusHype
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces—0
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
Designed Measurements for Vector Count Data—0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—0
Dictionary-Learning-Based Reconstruction Method for Electron Tomography—0
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness—0
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction—0
Adaptive Temporal Compressive Sensing for Video—0
A Compressive Sensing Based Method for Harmonic State Estimation—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