Abstract
Some advanced defogging algorithms can reconstruct most details of the image, but cause color anomaly, which is too saturated or seriously distorted in some local areas in the restored image. In this paper, we present a new framework for image defogging using step estimation of transmissivity. Firstly, we capitalize a binary tree algorithm to segment image successively and utilize the small image blocks after every iteration as the effective area to estimate the atmospheric light. Second, we set a threshold to separate the image into two parts: bright and dark region. For the dark region of the image, we calculated transmissivity on the basis of the dark channel prior and obtain adaptive transmissivity estimation in the bright region. The experimental results show that the algorithm can effectively solve halo and color distortion after defogging.
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Acknowledgements
This work has been supported by Guangdong Youth Innovation Talent Project (2016KQNCY204) and Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (pdjh2017b0927).
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Tang, J., Chen, Z., Su, B., Zheng, J. (2018). Single Image Defogging Based on Step Estimation of Transmissivity. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_8
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DOI: https://doi.org/10.1007/978-981-10-7389-2_8
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