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Proceedings Paper

Referenceless perceptual fog density prediction model
Author(s): Lark Kwon Choi; Jaehee You; Alan C. Bovik
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Paper Abstract

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.

Paper Details

Date Published: 25 February 2014
PDF: 12 pages
Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90140H (25 February 2014); doi: 10.1117/12.2036477
Show Author Affiliations
Lark Kwon Choi, The Univ. of Texas at Austin (United States)
Jaehee You, Hongik Univ. (Korea, Republic of)
Alan C. Bovik, The Univ. of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 9014:
Human Vision and Electronic Imaging XIX
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)

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