Mr. Lark Kwon Choi Profile

Lark Kwon Choi

at Univ of Texas at Austin

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Proceedings Article | 25 February 2014 Paper

Referenceless perceptual fog density prediction model

Lark Kwon Choi, Jaehee You, Alan Bovik

Proceedings Volume 9014, 90140H (2014) https://doi.org/10.1117/12.2036477

KEYWORDS: Fiber optic gyroscopes, Visibility through fog, Visibility, Statistical modeling, RGB color model, Performance modeling, Image enhancement, Light scattering, Cameras, Atmospheric modeling

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We propose a perceptual fog density prediction model based on natural scene statistics (NSS) and “fog aware” statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.

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