Model Based Edge-Preserving and Guided Filter for Real-World Hazy Scenes Visibility Restoration

Model Based Edge-Preserving and Guided Filter for Real-World Hazy Scenes Visibility Restoration Transmission estimation is the most challenging part for single image haze removal and very sensitive to environment noise. However, most existing single image dehazing algorithms are far from satisfactory in terms of restoring an image’s details and noise removal. To address this issue, an improved haze imaging model with transmission refinement based on dark channel prior is constructed to preserve the edge details and enhance visibility. Then, a fast single image dehazing algorithm called TSGA algorithm is proposed for complex real-world images. A refined transmission map obtained by TGVSH regularity scheme provides more edges and finer details and is less susceptible to noise. Guided filter and adaptive histogram equalization greatly enhance the visibility and color contrast of the scenes and significantly improve the drawback of halo artifacts. A large quantity of comparative experiment results demonstrate that the proposed algorithm simultaneously removes the serious effect of haze and noise, effectively makes the restored images look more natural, and has a lower time complexity. All these make it a good candidate for image segmentation, object recognition, and target tracking in complex real-world weather conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cognitive Computation Springer Journals

Model Based Edge-Preserving and Guided Filter for Real-World Hazy Scenes Visibility Restoration

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics
ISSN
1866-9956
eISSN
1866-9964
D.O.I.
10.1007/s12559-017-9458-4
Publisher site
See Article on Publisher Site

Abstract

Transmission estimation is the most challenging part for single image haze removal and very sensitive to environment noise. However, most existing single image dehazing algorithms are far from satisfactory in terms of restoring an image’s details and noise removal. To address this issue, an improved haze imaging model with transmission refinement based on dark channel prior is constructed to preserve the edge details and enhance visibility. Then, a fast single image dehazing algorithm called TSGA algorithm is proposed for complex real-world images. A refined transmission map obtained by TGVSH regularity scheme provides more edges and finer details and is less susceptible to noise. Guided filter and adaptive histogram equalization greatly enhance the visibility and color contrast of the scenes and significantly improve the drawback of halo artifacts. A large quantity of comparative experiment results demonstrate that the proposed algorithm simultaneously removes the serious effect of haze and noise, effectively makes the restored images look more natural, and has a lower time complexity. All these make it a good candidate for image segmentation, object recognition, and target tracking in complex real-world weather conditions.

Journal

Cognitive ComputationSpringer Journals

Published: Mar 20, 2017

References

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