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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 541–550 of 597 papers

TitleStatusHype
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based AlgorithmCode0
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative PriorsCode0
Provable Dynamic Robust PCA or Robust Subspace TrackingCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam AlignmentCode0
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Interpretable Recurrent Neural Networks Using Sequential Sparse RecoveryCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
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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