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
Feature-aware Label Space Dimension Reduction for Multi-label Classification—0
Fingerprint Recognition under Missing Image Pixels Scenario—0
Fractal Compressive Sensing—0
Forensic Discrimination between Traditional and Compressive Imaging Systems—0
Forest Sparsity for Multi-channel Compressive Sensing—0
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared—0
Frequency-Based Environment Matting by Compressive Sensing—0
Frequency-modulated continuous-wave LiDAR compressive depth-mapping—0
Compressive Sensing and Neural Networks from a Statistical Learning Perspective—0
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds—0
Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels—0
Generalized Optimization of High Capacity Compressive Imaging Systems—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation—0
Generative adversarial network for super-resolution imaging through a fiber—0
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives—0
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring—0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems—0
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing—0
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization—0
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization—0
Group Sparse Coding with a Laplacian Scale Mixture Prior—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Show:102550
← PrevPage 14 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