Results 321 to 330 of about 2,960,959 (361)

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system. [PDF]

J Biomed Opt
Haltmeier M, Ye M, Felbermayer K, Hinterleitner F, Burgholzer P.   +4 more
europepmc   +1 more source

A visual security multi-key selection image encryption algorithm based on a new four-dimensional chaos and compressed sensing. [PDF]

Sci Rep
Zhu S, Zhu C.
europepmc   +1 more source

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers. [PDF]

Eur Radiol Exp
Dratsch T, Zäske C, Siedek F, Rauen P, Hokamp NG, Sonnabend K, Maintz D, Bratke G, Iuga A.   +8 more
europepmc   +1 more source

Accelerating brain three-dimensional T2 fluid-attenuated inversion recovery using artificial intelligence-assisted compressed sensing: a comparison study with parallel imaging. [PDF]

Quant Imaging Med Surg
Ding J, Chai L, Duan Y, Wang Z, Miao C, Xiang S, Yang Y, Liu Y.   +7 more
europepmc   +1 more source

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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

DCSN: Deep Compressed Sensing Network for Efficient Hyperspectral Data Transmission of Miniaturized Satellite

IEEE Transactions on Geoscience and Remote Sensing, 2021
Requirements of compressed sensing techniques targeted at miniaturized hyperspectral satellite applications include lightweight onboard hardware, high-speed sensing, low sampling rate for compressing the massive volume of typical hyperspectral data, and ...
Chih-Chung Hsu, Chia-Hsiang Lin, Chao-Yuan Kao, Yen-Cheng Lin   +3 more
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

Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

IEEE Journal on Selected Topics in Signal Processing, 2007
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations.
Mário A. T. Figueiredo, R. Nowak, S. Wright   +2 more
semanticscholar   +1 more source

Compressed Sensing

, 2012
Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio ...
Gitta Kutyniok
semanticscholar   +1 more source

Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

IEEE Transactions on Image Processing, 2020
The total variation (TV) is a powerful regularization term encoding the local smoothness prior structure underlying images. By combining the TV regularization term with low rank prior, the 3D total variation (3DTV) regularizer has achieved advanced ...
Jiangjun Peng, Qi Xie, Qian Zhao, Yao Wang, L. Yee, Deyu Meng   +5 more
semanticscholar   +1 more source