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 271–280 of 597 papers

TitleStatusHype
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
Convex Reconstruction of Structured Matrix Signals from Linear Measurements (I): Theoretical Results—0
Block-wise Lensless Compressive Camera—0
A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing—0
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning—0
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—0
CONGO: Compressive Online Gradient Optimization—0
Composing Normalizing Flows for Inverse Problems—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