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 1–10 of 597 papers

TitleStatusHype
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models—0
RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation—0
Low-Complexity Super-Resolution Signature Estimation of XL-MIMO FMCW Radar—0
Discrete and Continuous Difference of Submodular MinimizationCode0
Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems—0
Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal RecoveryCode0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A Novel Radar Constant False Alarm Rate Detection Algorithm Based on VAMP Deep Unfolding—0
Secure Directional Modulation with Movable Antenna Array Aided by RIS—0
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All datasetsSet11 cs=50%BSD68 CS=50%BSDS100 - 2x upscalingUrban100 - 2x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AMPA-NetAverage PSNR36.33—Unverified