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Sensors 2015, 15(3), 6633-6651; doi:10.3390/s150306633

Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

1
Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea
2
Department of Satellite Data Cal/Val Team, Korea Aerospace Research Institute, 115 Gwahangbo,Yusung-Gu, Daejon 305-806, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 9 December 2014 / Revised: 13 February 2015 / Accepted: 10 March 2015 / Published: 19 March 2015
(This article belongs to the Special Issue UAV Sensors for Environmental Monitoring)
View Full-Text   |   Download PDF [21391 KB, uploaded 19 March 2015]   |  

Abstract

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. View Full-Text
Keywords: image dehazing; image defogging; image enhancement; unmanned aerial vehicle images; remote sensing images image dehazing; image defogging; image enhancement; unmanned aerial vehicle images; remote sensing images
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yoon, I.; Jeong, S.; Jeong, J.; Seo, D.; Paik, J. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images. Sensors 2015, 15, 6633-6651.

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