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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 597 papers

TitleStatusHype
Solving Linear Inverse Problems Using the Prior Implicit in a DenoiserCode1
Ensemble learning priors unfolding for scalable Snapshot Compressive SensingCode1
Dynamic Path-Controllable Deep Unfolding Network for Compressive SensingCode1
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed SensingCode1
Memory-Efficient Network for Large-scale Video Compressive SensingCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive SensingCode1
FSOINet: Feature-Space Optimization-Inspired Network for Image Compressive SensingCode1
SNIPS: Solving Noisy Inverse Problems StochasticallyCode1
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive SensingCode1
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximationCode1
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationCode1
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural NetworkCode1
Fast L1-Minimization Algorithms For Robust Face RecognitionCode0
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent VariablesCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRICode0
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid PrecodingCode0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
Discrete and Continuous Difference of Submodular MinimizationCode0
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratiosCode0
Towards improving discriminative reconstruction via simultaneous dense and sparse codingCode0
An Efficient Algorithm for Clustered Multi-Task Compressive SensingCode0
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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