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Haze editing with natural transmission

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Abstract

Significant efforts have been devoted to haze removal of outdoor scenic images and haze simulation of virtual scenes. However, few works focus on editing (increasing and decreasing) haze effects, which are common outdoor photography on real world images. In this paper, we present a dark channel prior-based transmission model that can explicitly formulates aerial perspective implying human perception on natural haze. We introduce maximum visibility as a parameter into the transmission model, so that we are able to naturally edit the amount of haze in an image by tuning this parameter with a physical interpretation. Additionally, we derive color correction and sky compensation from the transmission model, which improves the image quality for haze editing. Experimental results demonstrate the ability of the proposed method to generate images with various amounts of haze in a natural and efficient manner. Comparisons with the traditional algorithms on haze removal show the performance of the proposed algorithm in terms of two objective metrics that evaluate the visibility and fidelity of the restored images.

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Notes

  1. A MATLAB GUI program with source codes and testing images with results are available in supplemental materials.

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Acknowledgments

Xin Fan and Renjie Gao are supported by the Natural Science Foundation of China (NSFC) under grant Nos. 61272371 and the program for New Century Excellent Talents (NCET-11-0048). Yi Wang is supported by the NSFC under grant Nos. 61402072. Zhongxuan Luo is supported by the NSFC under grant Nos. 61033012 and 61328206. A short version of this paper is previously published at IEEE International Conference on Image Processing 2012 (ICIP’12).

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Authors and Affiliations

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

    Xin Fan, Yi Wang & Renjie Gao

  2. School of Software and School of Mathematical Sciences, Dalian University of Technology, Dalian, China

    Zhongxuan Luo

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  1. Xin Fan
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  2. Yi Wang
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  3. Renjie Gao
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  4. Zhongxuan Luo
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Correspondence to Xin Fan.

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Fan, X., Wang, Y., Gao, R. et al. Haze editing with natural transmission. Vis Comput 32, 137–147 (2016). https://doi.org/10.1007/s00371-015-1083-1

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  • DOI: https://doi.org/10.1007/s00371-015-1083-1

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