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 576–597 of 597 papers

TitleStatusHype
Multilinear Compressive LearningCode0
Multilinear Compressive Learning with Prior KnowledgeCode0
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement MatricesCode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive VideoCode0
Deep Geometric Distillation Network for Compressive Sensing MRICode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Invert: Signal Recovery via Deep Convolutional NetworksCode0
Generative Patch Priors for Practical Compressive Image RecoveryCode0
Multi-Scale Deep Compressive Sensing NetworkCode0
Multi-Scale Deep Compressive Sensing NetworkCode0
Compressive Closeness in NetworksCode0
Operational Support Estimator NetworksCode0
Group-based Sparse Representation for Image RestorationCode0
Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential EquationsCode0
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative PriorsCode0
Training Image Estimators without Image Ground-TruthCode0
Compressive Image Scanning MicroscopeCode0
Chasing Better Deep Image Priors between Over- and Under-parameterizationCode0
Difference of Convolution for Deep Compressive SensingCode0
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionCode0
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