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

doi:10.3969/j.issn.1000-1093.2019.07.012

Abstract

Abstract: 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. Key

Key words: imagedefogging, opticaldepth, fogdensity, surrogatemodel

CLC Number:

TP391.413

LI Chunming， JIANG Yutong， SONG Haiping， JI Chao， GUO Meng， ZHU Lin. Research on Optical Depth Surrogate Model-based Method for Estimating Fog Density and Removing Fog Effect from Images[J]. Acta Armamentarii, 2019, 40(7): 1425-1433.

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第40卷
第7期2019 年7月兵工学报ACTA
ARMAMENTARIIVol.40No.7Jul.2019

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