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 451–460 of 597 papers

TitleStatusHype
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—0
High SNR Consistent Compressive Sensing—0
Sparse Depth Sensing for Resource-Constrained RobotsCode0
DR2-Net: Deep Residual Reconstruction Network for Image Compressive SensingCode0
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix—0
Block-wise Lensless Compressive Camera—0
Learning to Invert: Signal Recovery via Deep Convolutional NetworksCode0
Compressive Sensing via Convolutional Factor Analysis—0
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive SensingCode0
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction—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