# Compressive Sensing   Edit

64 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.

# Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

7 Jun 2021

The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.

# Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing

7 Jun 2021

In many scenarios, the number of defects that we are interested in is limited and the signaling response of the layered structure can be modeled as a low-rank structure.

# Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet Frames

1 Jun 2021

The signal is modeled by an arbitrary SI generator whose special case is the box function, which, as we show in the paper, is conventionally used in single-pixel imaging.

# Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

26 May 2021

Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing context-awareness to a wider range of devices.

# Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples

26 May 2021

In this paper, we develop a regularization method that outperforms compressive sensing methods as well as selected learning-based methods, without any need for training data.

# Learning Generative Prior with Latent Space Sparsity Constraints

25 May 2021

We also consider the effect of the dimension of the latent space and the sparsity factor in validating the SDLSS framework.

# Reinforcement Learning for Adaptive Video Compressive Sensing

18 May 2021

We apply reinforcement learning to video compressive sensing to adapt the compression ratio.

# Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives

4 May 2021

However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical.

# Weighed $\ell_1$ on the simplex: Compressive sensing meets locality

28 Apr 2021

Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks.

# CAIM: Cooperative Angle of Arrival Estimation using the Ising Method

27 Apr 2021

This paper proposes a cooperative angle-of-arrival(AoA) estimation, taking advantage of co-processing channel state information (CSI) from a group of access points that receive signals of the same source.