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 326–350 of 597 papers

TitleStatusHype
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning—0
Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values—0
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models—0
Multi-Channel Deep Networks for Block-Based Image Compressive SensingCode0
A Bayesian Lasso based Sparse Learning Model—0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization—0
Review of Algorithms for Compressive Sensing of Images—0
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs—0
Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Fast and Provable ADMM for Learning with Generative Priors—0
Regularizing linear inverse problems with convolutional neural networks—0
New ECCM Techniques Against Noise-like and/or Coherent Interferers—0
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing—0
Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO—0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
A data-driven approach to sampling matrix selection for compressive sensing—0
Compressive Closeness in NetworksCode0
Training Image Estimators without Image Ground-TruthCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
A Compressive Sensing Video dataset using Pixel-wise coded exposure—0
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas—0
Multilinear Compressive LearningCode0
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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