Infrared Image Super-Resolution with Parallel Random Forest

Infrared imaging has the advantage of all-weather working ability. Due to the limitation of the hardware and the high cost, the resolution of infrared image (IR) is very low. To improve the resolution of IR images, this paper exploits super-resolution (SR) method for IR images. A new SR framework by...

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Veröffentlicht in:	International journal of parallel programming Jg. 46; H. 5; S. 838 - 858
Hauptverfasser:	Yang, Xiaomin, Wu, Wei, Yan, Binyu, Wang, Huiqian, Zhou, Kai, Liu, Kai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2018 Springer Nature B.V
Schlagworte:	Computer Science Economic models Feature extraction Image resolution Infrared imagery Infrared imaging Machine learning Processor Architectures Regression models Software Engineering/Programming and Operating Systems Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm Theory of Computation Training Feature extraction Infrared super-resolution Parallel random forest Infrared image
ISSN:	0885-7458, 1573-7640
Online-Zugang:	Volltext
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