Haze Visibility Enhancement: A Survey and Quantitative Benchmarking

July 21, 2016 ยท The Cartographer ยท ๐Ÿ› Computer Vision and Image Understanding

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: Haze Visibility Enhancement: A Survey and Quantitative Benchmarking"

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Authors Yu Li, Shaodi You, Michael S. Brown, Robby T. Tan arXiv ID 1607.06235 Category cs.CV: Computer Vision Citations 161 Venue Computer Vision and Image Understanding Last Checked 1 day ago
Abstract
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.
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