Compressive Sensing

107 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


Use these libraries to find Compressive Sensing models and implementations

Most implemented papers

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

xchen-tamu/linear-lista-cpss NeurIPS 2018

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

sssohrab/PhDthesis 24 Jan 2019

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

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

caiyuanhao1998/MST CVPR 2022

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

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

PSCLab-ASU/CSVideoNet 15 Dec 2016

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

rick-chang/OneNet 29 Mar 2017

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

andrewssobral/lrslibrary 24 May 2017

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

Multilinear Compressive Learning

viebboy/MultilinearCompressiveLearningFramework 17 May 2019

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

GauriJagatap/invimaging-deeppriors NeurIPS 2019

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

psycholsc/complex-DnCNN 3 Jun 2020

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.

Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices

pengxiawu/DeepBP-AE 10 Jul 2020

Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.