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Fast example searching for input-adaptive data-driven dehazing with Gaussian process regression

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Abstract

Recently, data-driven approaches are prevailing in low-level image processing including single image dehazing. The performance of these methods can behave better when the learning process adapts to the input. This input-adaptive training demands efficiently selecting optimal examples for the input from a large training set. In this paper, we address the issue of input-specific example searching and propose a fast searching strategy on vast image examples to learn a more accurate Gaussian process (GP) regressor for single image dehazing. The GP regression learnt from these optimal examples is able to produce the transmission prediction with lower variance and thus renders high robustness. Extensive experiments on hazy images at various haze levels demonstrate the effectiveness of the proposed example searching compared with the state-of-the-art data-driven dehazing methods.

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Notes

  1. All resultant images for these comparisons and testing hazy images are available at https://github.com/dlut-dimt/TVCJ.

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Acknowledgements

This work is partially supported by the Natural Science Foundation of China under Grant Nos. 61572096, 61432003, and 61733002. The authors are grateful to Prof. Ming-Ting Sun at the University of Washington and Dr. Jue Wang at Megvii Inc. for their constructive discussions and suggestions.

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Authors and Affiliations

  1. DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, China

    Xin Fan

  2. School of Software, Dalian University of Technology, Dalian, China

    Xianxuan Tang, Minjun Hou & Zhongxuan Luo

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  1. Xin Fan
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  2. Xianxuan Tang
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  4. Zhongxuan Luo
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Correspondence to Xin Fan.

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Fan, X., Tang, X., Hou, M. et al. Fast example searching for input-adaptive data-driven dehazing with Gaussian process regression. Vis Comput 35, 565–577 (2019). https://doi.org/10.1007/s00371-018-1485-y

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