Compressive Sensing
109 papers with code • 5 benchmarks • 4 datasets
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
Benchmarks
These leaderboards are used to track progress in Compressive Sensing
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Libraries
Use these libraries to find Compressive Sensing models and implementations
BSD
Urban100
Set5
Set11
Latest papers with no code
Compression Ratio Learning and Semantic Communications for Video Imaging
In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself.
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency.
Fractal Compressive Sensing
Compare reconstruction quality of the sampling schemes under various reconstruction strategies to determine the suitability of p. frac for CS-MRI.
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot.
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding
Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL$^*$ condition to hold.
Multi UAV-enabled Distributed Sensing: Cooperation Orchestration and Detection Protocol
This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target, and UAVs operate in half-duplex mode.
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing
Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process.
Sparse Models for Machine Learning
The sparse modeling is an evident manifestation capturing the parsimony principle just described, and sparse models are widespread in statistics, physics, information sciences, neuroscience, computational mathematics, and so on.
In-sector Compressive Beam Alignment for MmWave and THz Radios
The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of the CS matrix for in-sector channel estimation.
Coded Aperture Radar Imaging Using Reconfigurable Intelligent Surfaces
In this paper, we focus on radar imaging using active sensing with a single transceiver and reconfigurable intelligent surface (RIS).
