Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks
Joint Authors
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-11-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Nowadays many camera-based advanced driver assistance systems (ADAS) have been introduced to assist the drivers and ensure their safety under various driving conditions.
One of the problems faced by drivers is the faded scene visibility and lower contrast while driving in foggy conditions.
In this paper, we present a novel approach to provide a solution to this problem by employing deep neural networks.
We assume that the fog in an image can be mathematically modeled by an unknown complex function and we utilize the deep neural network to approximate the corresponding mathematical model for the fog.
The advantages of our technique are as follows: (i) its real-time operation and (ii) being based on minimal input, that is, a single image, and exhibiting robustness/generalization for various unseen image data.
Experiments carried out on various synthetic images indicate that our proposed technique has the abilities to approximate the corresponding fog function reasonably and remove it for better visibility and safety.
American Psychological Association (APA)
Hussain, Farhan& Jeong, Jechang. 2015. Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks. Journal of Sensors،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110440
Modern Language Association (MLA)
Hussain, Farhan& Jeong, Jechang. Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks. Journal of Sensors No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1110440
American Medical Association (AMA)
Hussain, Farhan& Jeong, Jechang. Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks. Journal of Sensors. 2015. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110440
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110440