The optimally designed autoencoder network for compressed sensing

Compressed sensing (CS) is a signal processing framework, which reconstructs a signal from a small set of random measurements obtained by measurement matrices. Due to the strong randomness of measurement matrices, the reconstruction performance is unstable. Additionally, current reconstruction algor...

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Veröffentlicht in:	EURASIP journal on image and video processing Jg. 2019; H. 1; S. 1 - 12
Hauptverfasser:	Zhang, Zufan, Wu, Yunfeng, Gan, Chenquan, Zhu, Qingyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 29.04.2019 Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Biometrics Compressed sensing Data recovery Deep learning Detection Engineering Image Processing and Computer Vision Machine learning Multiple nonlinear measurement Noise reduction Pattern Recognition Process parameters Reconstruction Signal processing Signal reconstruction Signal,Image and Speech Processing Stacked sparse denoising autoencoder Training Stacked sparse denoising autoencoder Deep learning Multiple nonlinear measurement Signal reconstruction Compressed sensing
ISSN:	1687-5281, 1687-5176, 1687-5281
Online-Zugang:	Volltext
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