access icon free Visibility distance estimation in foggy situations and single image dehazing based on transmission computation model

© The Institution of Engineering and Technology
Received 01/04/2017, Accepted 05/03/2018, Revised 23/01/2018, Published 09/03/2018

The existing visibility distance estimation algorithms in foggy situations use the region growing method to extract the vertical position of inflection point of image intensity changing. These algorithms have lower inflection point location accuracy for an image with a non-homogeneous road surface. To deal with these problems, this study presents a novel visibility distance measuring technique under foggy weather conditions. This method combines two major models: inflection point estimation (IPE) model and transmission refining (TR) model. The proposed IPE model based on transmission computation model derives a very useful relation between the transmission value of inflection points and the constant . In order to acquire the more accurate transmission map and vertical position of each inflection point, this study establishes an effective TR model. This model exploits the edge information of input images, in order to significantly reduce the effects of artefact. The proposed algorithm provides more accurate visibility distance estimation of an image with a non-homogeneous road surface than the well-known algorithm through qualitative evaluations in experiments. The experimental results also show that the TR model has better outcomes than the guided filter approach through qualitative and quantitative evaluations.

Inspec keywords: geophysical image processing; image segmentation; distance measurement; atmospheric techniques; visibility; image enhancement; fog

Other keywords: transmission map; IPE model; foggy weather conditions; foggy situations; TR model; guided filter approach; image intensity; nonhomogeneous road surface; transmission refining model; visibility distance estimation; transmission computation model; inflection point estimation model

Subjects: Geophysics computing; Spatial variables measurement; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Computer vision and image processing techniques; Clouds, fog, haze, aerosols, effects of pollution on atmospheric optics; Atmospheric, ionospheric and magnetospheric techniques and equipment; Optical, image and video signal processing; Spatial variables measurement

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