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 351–375 of 597 papers

TitleStatusHype
Group Sparsity Methods for Compressive Space-Frequency Channel Estimation and Spatial Equalization in Fluid Antenna System—0
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging—0
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing—0
High-Dimensional Confidence Regions in Sparse MRI—0
High SNR Consistent Compressive Sensing—0
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
How to find real-world applications for compressive sensing—0
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration—0
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior—0
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet—0
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing—0
Identifying Unused RF Channels Using Least Matching Pursuit—0
_1DecNet+: A new architecture framework by _1 decomposition and iteration unfolding for sparse feature segmentation—0
Image Classification with A Deep Network Model based on Compressive Sensing—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization—0
Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization—0
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix—0
Image Restoration from Patch-based Compressed Sensing Measurement—0
Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters—0
Improved Coherence Index-Based Bound in Compressive Sensing—0
Information-Theoretic Bounds for Adaptive Sparse Recovery—0
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models—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