# Compressive Sensing

114 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

## Libraries

Use these libraries to find Compressive Sensing models and implementations## Most implemented papers

# Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

The HSI representations are highly similar and correlated across the spectral dimension.

# Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

In this work, we study unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery.

# Learning to compress and search visual data in large-scale systems

The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective.

# IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.

# CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing

This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS).

# One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models

On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.

# Provable Dynamic Robust PCA or Robust Subspace Tracking

Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).

# Multilinear Compressive Learning

Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.

# Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

Specifically, we consider the problem of solving linear inverse problems, such as compressive sensing, as well as non-linear problems, such as compressive phase retrieval.

# Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput.