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 451–475 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
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing—0
CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive SensingCode0
Design of Image Matched Non-Separable Wavelet using Convolutional Neural Network—0
Active Search for Sparse Signals with Region Sensing—0
Deep ADMM-Net for Compressive Sensing MRI—0
Fast recovery from a union of subspaces—0
Analyzing the group sparsity based on the rank minimization methods—0
Interpretable Recurrent Neural Networks Using Sequential Sparse RecoveryCode0
Detecting Breast Cancer using a Compressive Sensing Unmixing Algorithm—0
Lensless Imaging with Compressive Ultrafast Sensing—0
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach—0
An Efficient Method for Robust Projection Matrix DesignCode0
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier—0
Adaptive foveated single-pixel imaging with dynamic super-sampling—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