Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth.

Abstract

In order to estimate fog density correctly and to remove fog from foggy images appropriately, a surrogate model for optical depth is presented in this paper. We comprehensively investigate various fog-relevant features and propose a novel feature based on the hue, saturation, and value color space, which correlate well with the perception of fog density. We use a surrogate-based method to learn a refined polynomial regression model for optical depth with informative fog-relevant features, such as dark-channel, saturation-value, and chroma, which are selected on the basis of sensitivity analysis. Based on the obtained accurate surrogate model for optical depth, an effective method for fog density estimation and image defogging is proposed. The effectiveness of our proposed method is verified quantitatively and qualitatively by the experimental results on both synthetic and real-world foggy images.