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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	EURASIP journal on image and video processing Ročník 2019; číslo 1; s. 1 - 12
Hlavní autori:	Zhang, Zufan, Wu, Yunfeng, Gan, Chenquan, Zhu, Qingyi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 29.04.2019 Springer Nature B.V SpringerOpen
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!