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.
Highlights
Acquisition of high-quality images is an important issue in securing visual information of unmanned aerial vehicle (UAV) platforms
We address issues of the limitations and problems of the dark channel prior-based methods and justify the need of the wavelength-adaptive model of hazy image formulation
The dark channel prior has played an important role in various dehazing algorithms, the related methods suffer from color distortion and edge degradation, since they do not consider the wavelength characteristics in the image degradation model
Summary
Acquisition of high-quality images is an important issue in securing visual information of unmanned aerial vehicle (UAV) platforms. Yeh et al proposed a fast dehazing algorithm by analyzing the haze density based on pixel-level dark and bright channel priors [7]. The dark channel prior has played an important role in various dehazing algorithms, the related methods suffer from color distortion and edge degradation, since they do not consider the wavelength characteristics in the image degradation model. Wen computed the scattering and transmission factors of light in underwater images and successfully removed haze using the difference of light attenuation Yoon et al proposed color preserved defogging algorithms by considering the wavelength dependency [23,24]. The proposed dehazing method can significantly increase the visual quality of an atmospherically-degraded UAV image in the sense of preserving color distortion without the halo effect.
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