Results 301 to 310 of about 3,086,556 (345)

Compressed sensing reconstruction for high-SNR, rapid dissolved ¹²⁹Xe gas exchange MRI. [PDF]

Magn Reson Med
Pilgrim-Morris JH, Collier GJ, Munro RS, Norquay G, Stewart NJ, Wild JM.   +5 more
europepmc   +1 more source

AI-Assisted Compressed Sensing Enables Faster Brain MRI for the Elderly: Image Quality and Diagnostic Equivalence with Conventional Imaging. [PDF]

Int J Gen Med
Gu W, Yang C, Wang Y, Hu W, Wu D, Cai S, Hong G, Hu P, Zhang Q, Dai Y.   +9 more
europepmc   +1 more source

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Compressed Sensing

2012
Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining ...
Gitta Kutyniok
openaire   +2 more sources

Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing

arXiv.org, 2023
Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions.
Zhenyu Xiao, Songqi Cao, Lipeng Zhu, Yanming Liu, Xianggen Xia, Rui Zhang   +5 more
semanticscholar   +1 more source

Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends

IEEE Geoscience and Remote Sensing Magazine, 2022
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF).
Gang Xu, Bangjie Zhang, Hanwen Yu, Jianlai Chen, M. Xing, Wei Hong   +5 more
semanticscholar   +1 more source

Content-Aware Scalable Deep Compressed Sensing

IEEE Transactions on Image Processing, 2022
To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction ...
Bin Chen, Jian Zhang
semanticscholar   +1 more source

Image Compressed Sensing Using Convolutional Neural Network

IEEE Transactions on Image Processing, 2020
In the study of compressed sensing (CS), the two main challenges are the design of sampling matrix and the development of reconstruction method. On the one hand, the usually used random sampling matrices (e.g., GRM) are signal independent, which ignore ...
Wuzhen Shi, F. Jiang, Shaohui Liu, Debin Zhao   +3 more
semanticscholar   +1 more source

Communication-Efficient Federated Learning Based on Compressed Sensing

IEEE Internet of Things Journal, 2021
In this article, we investigate the problem of federated learning (FL) in a communication-constrained environment of the Internet of Things (IoT), where multiple IoT clients train a global model collectively by communicating model updates with a central ...
Chengxi Li, Gang Li, P. Varshney
semanticscholar   +1 more source