Image Dehazing Method Based on Multi-scale Feature Fusion

DOI

10.2991/essaeme-17.2017.438How to use a DOI?

Keywords

Image dehazing, adaptive clustering, feature fusion, Laplacian pyramid

Abstract

In recent years, image dehazing has become an active topic in computer vision. This paper presents a novel dehazing method based on adaptive clustering and image fusion for restoring a single image degraded by fog or haze. Firstly, the coarse-scale transmission map is addressed based on adaptive clustering for the haze image. Then, the saturation of haze image is extracted as fine-scale transmission map, which can reflect the scene depth information truly and naturally. Next, the accurate transmission map is generated by using Laplacian pyramid fusion technique. Finally, the haze-free image is restored by solving the image degradation physical model. The effectiveness of the proposed method is evaluated on various real-scene haze images. Experimental results indicate that our proposed method is simpler and more effective compared with several well-known methods, and even achieve better visual results. Specifically, our method is more fast and suitable for real-time requirement.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Minghai Yao
AU  - Qi Miao
AU  - Qiaohong Hao
PY  - 2017/07
DA  - 2017/07
TI  - Image Dehazing Method Based on Multi-scale Feature Fusion
BT  - Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/essaeme-17.2017.438
DO  - 10.2991/essaeme-17.2017.438
ID  - Yao2017/07
ER  -