Research on Optical Depth Surrogate Model-based Method for Estimating Fog Density and Removing Fog Effect from Images

被引：0

作者：

Li C. ^{[1
]}

Jiang Y. ^{[1
]}

Song H. ^{[1
]}

Ji C. ^{[1
]}

Guo M. ^{[1
]}

Zhu L. ^{[1
]}

机构：

[1] China North Vehicle Research Institute, Beijing

来源：

Binggong Xuebao/Acta Armamentarii | 2019年 / 40卷 / 07期

关键词：

Fog density; Image defogging; Optical depth; Surrogate model;

D O I：

10.3969/j.issn.1000-1093.2019.07.012

中图分类号：

学科分类号：

摘要：

The images are vulnerable to suspended particles in the atmosphere, and the quality of image captured in fog is deteriorated to lead to many difficulties for battlefield reconnaissance and recognition. Various fog-related image features are investigated, and a novel feature based on chroma, saturation, and Brightness value color space which correlates well with the fog density is proposed. The surrogate-based method is used to build a refined polynomial regression model for optical depth with informative fog-related features, including the dark-channel, saturation-value, and chroma. An effective method for fog density estimation and image defogging is proposed based on the surrogate model. Experimental validations prove that the proposed method has better effect than conventional methods in both quantitation and quality, and leads to great improvements in real-time image defogging. © 2019, Editorial Board of Acta Armamentarii. All right reserved.

引用

页码：1425 / 1433

页数：8

共 15 条

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