Abstract
In scene dehazing problem, single image haze prediction is one of the most challenging issues. In this paper, we propose a hybrid features learning model (HFLM) for haze prediction. HFLM takes a hazy image as the input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. There are two main stages in HFLM. The first stage is used to extract haze-related features from haze images. The second stage aims to establish the mapping relationship between features and medium transmission. In the experimental part, we explore the hyper-parameters in support vector and verify the significance of the features selection. Further, we compare our method with other dehazing methods and make a qualitative comparison on synthetic images. Demonstrate our method has more superior performance than the state-of-the-art dehazing methods.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61502541, 61502546, 61772140), the Natural Science Foundation of Guangdong Province (2016A030310202), the Fundamental Research Funds for the Central Universities (Sun Yat-sen University, 16lgpy39), and the Science and Technology Planning Project of Guangdong Province (2015B010129008).
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Yan, J., Liang, X., Lin, M. et al. A hybrid features learning model for single image haze prediction. SIViP 12, 1001–1008 (2018). https://doi.org/10.1007/s11760-018-1245-5
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DOI: https://doi.org/10.1007/s11760-018-1245-5