Visibility distance estimation in foggy situations and single image dehazing based on transmission computation model. Issue 7 (1st July 2018)

• Record Type: Journal Article
• Authors: Liu, Jianlei
• Is Part Of: IET image processing. Volume 12:Issue 7(2018)
• Page Start: 1237
• Page End: 1244
• Publication Date: 2018-07-01
• Subjects: image segmentation -- image enhancement -- distance measurement -- visibility -- atmospheric techniques -- geophysical image processing -- fog; nonhomogeneous road surface -- foggy weather conditions -- inflection point estimation model -- transmission refining model -- IPE model -- transmission computation model -- foggy situations -- image intensity -- transmission map -- TR model -- visibility distance estimation -- guided filter approach; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0323
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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 e − 2 . 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.