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 476–500 of 597 papers

TitleStatusHype
Onsager-corrected deep learning for sparse linear inverse problems—0
DeepBinaryMask: Learning a Binary Mask for Video Compressive SensingCode0
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets—0
Masking Strategies for Image Manifolds—0
Sketching for Large-Scale Learning of Mixture Models—0
Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources—0
Compressive phase-only filtering at extreme compression rates—0
Deep Fully-Connected Networks for Video Compressive SensingCode0
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography—0
Multi-resolution Compressive Sensing Reconstruction—0
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing—0
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields—0
Compressive hyperspectral imaging via adaptive sampling and dictionary learning—0
Frequency-Based Environment Matting by Compressive Sensing—0
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior—0
Fast and Accurate Head Pose Estimation via Random Projection Forests—0
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing—0
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm—0
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation—0
Quantifying the influence of conformational uncertainty in biomolecular solvation—0
Compressive Sensing via Low-Rank Gaussian Mixture Models—0
Distributed Compressive Sensing: A Deep Learning Approach—0
A Deep Learning Approach to Structured Signal Recovery—0
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing—0
Toward a Robust Sparse Data Representation for Wireless Sensor Networks—0
Show:102550
← PrevPage 20 of 24Next →
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