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

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Showing 426–450 of 597 papers

TitleStatusHype
Bayesian Compressive Sensing Using Normal Product Priors—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices—0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT—0
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks—0
Experimental comparison of single-pixel imaging algorithms—0
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring—0
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive SensingCode0
Two-pixel polarimetric camera by compressive sensing—0
Measurement-Adaptive Sparse Image Sampling and Recovery—0
Nonconvex penalties with analytical solutions for one-bit compressive sensing—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Restoration from Patch-based Compressed Sensing Measurement—0
Towards Understanding the Invertibility of Convolutional Neural Networks—0
Provable Dynamic Robust PCA or Robust Subspace TrackingCode0
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI—0
Deep De-Aliasing for Fast Compressive Sensing MRI—0
Sub-Pixel Registration of Wavelet-Encoded Images—0
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences—0
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration—0
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization—0
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection ModelsCode0
Multilinear compressive sensing and an application to convolutional linear networks—0
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation—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