Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks

Title
Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks
Authors
정제창
Keywords
VISION
Issue Date
2015-05
Publisher
HINDAWI PUBLISHING CORPORATION
Citation
JOURNAL OF SENSORS, v. 2016, Page. 1-2
Abstract
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.
URI
https://www.hindawi.com/journals/js/2016/3894832/http://hdl.handle.net/20.500.11754/24637
ISSN
1687-725X; 1687-7268
DOI
http://dx.doi.org/10.1155/2016/3894832
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > DEPARTMENT OF ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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