Referenceless perceptual image defogging
Lark Kwon Choi, Jaehee You, Alan Conrad Bovik · 2014
We propose a referenceless perceptual defog and visibility enhancement model based on multiscale “fog aware” statistical features. Our model operates on a single foggy image and uses a set of “fog aware” weight maps to improve the visibility of foggy regions. The proposed defog and visibility enhancer makes use of statistical regularities observed in foggy and fog-free images to extract the most visible information from three processed image results: one white balanced and two contrast enhanced images. Perceptual fog density, fog aware luminance, contrast, saturation, chrominance, and saliency weight maps smoothly blend these via a Laplacian pyramid. Evaluation on a variety of foggy images shows that the proposed model achieves better results for darker, denser foggy images as well as on standard defog test images.