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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Bibliographic Details
Published in:International journal of parallel programming Vol. 46; no. 5; pp. 838 - 858
Main Authors: Yang, Xiaomin, Wu, Wei, Yan, Binyu, Wang, Huiqian, Zhou, Kai, Liu, Kai
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2018
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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 using random forests is proposed in this paper. Existing methods adopts single regression model for SR. However, which single regression model tends to overfit training data, and would lead to a poor performance. Furthermore, the existing methods are not suitable for real-time system due to the heavy time consuming. To resolve this problem, an ensemble regression model, i.e. random forests rather than single regression model is adopted in this paper. In addition, to achieve better results multi-regression models rather than a single regression model are trained on the clustered training data. Moreover, the features used in many SR methods cannot extract features on diagonal orientation. To resolve this problem, we adopt a second order derivative filter, which can extract features on diagonal orientation. The experimental results demonstrate the availability of the proposed method.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0551-9