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From Global to Local: An Adaptive Environmental Illumination Estimation for Non-uniform Scattering

Published: 01 January 2024 Publication History

Abstract

The atmospheric scattering model is one of the most widely used model to describe the optical imaging processing of hazy images. However, the global atmospheric light used in the traditional atmospheric scattering model has limitations in describing images with varying local environmental illumination. In this paper, by extending the global atmospheric light to the local illumination, a non-uniform scattering model is proposed, which can better describe real scenes under non-uniform environmental illumination. Based on this model, an adaptive local illumination estimation for hazy image is proposed, which can adapt to the local differences of environment illumination. The experimental results demonstrate that the proposed algorithm can outperform the state-of-the-art algorithms in terms of not only the non-uniform scattering removal but also the adaptability.

References

[1]
Codruta O Ancuti, Cosmin Ancuti, Radu Timofte, and Christophe De Vleeschouwer. 2018. O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 754–762.
[2]
Zahra Anvari and Vassilis Athitsos. 2020. Dehaze-GLCGAN: unpaired single image de-hazing via adversarial training. arXiv preprint arXiv:2008.06632 (2020).
[3]
Dana Berman, Shai Avidan, 2016. Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1674–1682.
[4]
Dana Berman, Tali Treibitz, and Shai Avidan. 2017. Air-light estimation using haze-lines. In 2017 IEEE International Conference on Computational Photography (ICCP). IEEE, 1–9.
[5]
Trung Minh Bui and Wonha Kim. 2017. Single image dehazing using color ellipsoid prior. IEEE Transactions on Image Processing 27, 2 (2017), 999–1009.
[6]
Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. 2016. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing 25, 11 (2016), 5187–5198.
[7]
Tianyi Chen, Jiahui Fu, Wentao Jiang, Chen Gao, and Si Liu. 2021. SRKTDN: Applying super resolution method to dehazing task. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 487–496.
[8]
Shuangyu Cheng and Bin Yang. 2022. An efficient single image dehazing algorithm based on transmission map estimation with image fusion. Engineering Science and Technology, an International Journal 35 (2022), 101190.
[9]
Runmin Cong, Jianjun Lei, Huazhu Fu, Ming-Ming Cheng, Weisi Lin, and Qingming Huang. 2018. Review of visual saliency detection with comprehensive information. IEEE Transactions on circuits and Systems for Video Technology 29, 10 (2018), 2941–2959.
[10]
Xuan Dong, Yi Pang, and Jiangtao Wen. 2010. Fast efficient algorithm for enhancement of low lighting video. In ACM SIGGRAPH 2010 Posters. 1–1.
[11]
Deniz Engin, Anil Genç, and Hazim Kemal Ekenel. 2018. Cycle-dehaze: Enhanced cyclegan for single image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 825–833.
[12]
Masud An-Nur Islam Fahim and Ho Yub Jung. 2020. Single image dehazing using end-to-end deep-dehaze network. In The 9th International Conference on Smart Media and Applications. 158–163.
[13]
Yuanyuan Gao, Hai-Miao Hu, Bo Li, and Qiang Guo. 2017. Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex. IEEE Transactions on Multimedia 20, 2 (2017), 335–344.
[14]
Nicolas Hautiere, Jean-Philippe Tarel, Didier Aubert, and Eric Dumont. 2008. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology 27, 2 (2008), 87–95.
[15]
Kaiming He, Jian Sun, and Xiaoou Tang. 2010. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence 33, 12 (2010), 2341–2353.
[16]
Kaiming He, Jian Sun, and Xiaoou Tang. 2012. Guided image filtering. IEEE transactions on pattern analysis and machine intelligence 35, 6 (2012), 1397–1409.
[17]
Hai-Miao Hu, Hongda Zhang, Zichen Zhao, Bo Li, and Jin Zheng. 2019. Adaptive single image dehazing using joint local-global illumination adjustment. IEEE Transactions on Multimedia 22, 6 (2019), 1485–1495.
[18]
Zheyan Jin, Shiqi Chen, Yueting Chen, Zhihai Xu, and Huajun Feng. 2023. Let Segment Anything Help Image Dehaze. arXiv preprint arXiv:2306.15870 (2023).
[19]
Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. 1997. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing 6, 7 (1997), 965–976.
[20]
Mingye Ju, Can Ding, Charles A Guo, Wenqi Ren, and Dacheng Tao. 2021. IDRLP: Image dehazing using region line prior. IEEE Transactions on Image Processing 30 (2021), 9043–9057.
[21]
Mingye Ju, Can Ding, Wenqi Ren, Yi Yang, Dengyin Zhang, and Y Jay Guo. 2021. IDE: Image dehazing and exposure using an enhanced atmospheric scattering model. IEEE Transactions on Image Processing 30 (2021), 2180–2192.
[22]
Edwin H Land. 1977. The retinex theory of color vision. Scientific american 237, 6 (1977), 108–129.
[23]
Edwin H Land and John J McCann. 1971. Lightness and retinex theory. Josa 61, 1 (1971), 1–11.
[24]
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. 2017. Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision. 4770–4778.
[25]
Chongyi Li, Runmin Cong, Junhui Hou, Sanyi Zhang, Yue Qian, and Sam Kwong. 2019. Nested network with two-stream pyramid for salient object detection in optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 57, 11 (2019), 9156–9166.
[26]
Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang. 2022. Towards multi-domain single image dehazing via test-time training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5831–5840.
[27]
Dengsheng Lu and Qihao Weng. 2007. A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing 28, 5 (2007), 823–870.
[28]
Yong Luo, Tongliang Liu, Dacheng Tao, and Chao Xu. 2014. Decomposition-based transfer distance metric learning for image classification. IEEE Transactions on Image Processing 23, 9 (2014), 3789–3801.
[29]
Kresimir Matkovic, László Neumann, Attila Neumann, Thomas Psik, Werner Purgathofer, 2005. Global contrast factor-a new approach to image contrast. In CAe. 159–167.
[30]
Earl J McCartney. 1976. Optics of the atmosphere: scattering by molecules and particles. New York (1976).
[31]
William Edgar Knowles Middleton. 1957. Vision through the atmosphere. Springer.
[32]
Srinivasa G. Narasimhan and Shree K. Nayar. 2003. Contrast restoration of weather degraded images. IEEE transactions on pattern analysis and machine intelligence 25, 6 (2003), 713–724.
[33]
Ana Belén Petro, Catalina Sbert, and Jean-Michel Morel. 2014. Multiscale retinex. Image Processing On Line (2014), 71–88.
[34]
ILLUMINATION USING BRIGHT CHANNEL PRIOR. 2013. Single image de-haze under non-uniform illumination using bright channel prior. Journal of Theoretical and Applied Information Technology 48, 3 (2013), 1843–1848.
[35]
Wen Qian, Chao Zhou, and Dengyin Zhang. 2020. FAOD-Net: a fast AOD-Net for dehazing single image. Mathematical Problems in Engineering 2020 (2020), 1–11.
[36]
Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. 2016. Single image dehazing via multi-scale convolutional neural networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. Springer, 154–169.
[37]
Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated fusion network for single image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3253–3261.
[38]
Geet Sahu, Ayan Seal, Anis Yazidi, and Ondrej Krejcar. 2022. A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board. IEEE Transactions on Automation Science and Engineering (2022).
[39]
Ling-Feng Shi, Bo-Hao Chen, Shih-Chia Huang, Alexander Olegovich Larin, Oleg Sergeevich Seredin, Andrei Valerievich Kopylov, and Sy-Yen Kuo. 2018. Removing haze particles from single image via exponential inference with support vector data description. IEEE Transactions on Multimedia 20, 9 (2018), 2503–2512.
[40]
J Alex Stark. 2000. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on image processing 9, 5 (2000), 889–896.
[41]
Sabine E Susstrunk and Stefan Winkler. 2003. Color image quality on the internet. In Internet imaging V, Vol. 5304. SPIE, 118–131.
[42]
Robby T Tan. 2008. Visibility in bad weather from a single image. In 2008 IEEE conference on computer vision and pattern recognition. IEEE, 1–8.
[43]
Nian Wang, Zhigao Cui, Yanzhao Su, Chuan He, Yunwei Lan, and Aihua Li. 2021. Prior-guided multiscale network for single-image dehazing. IET Image Processing 15, 13 (2021), 3368–3379.
[44]
Wencheng Wang, Xiaohui Yuan, Xiaojin Wu, and Yunlong Liu. 2017. Fast image dehazing method based on linear transformation. IEEE Transactions on Multimedia 19, 6 (2017), 1142–1155.
[45]
Yongzhen Wang, Xuefeng Yan, Fu Lee Wang, Haoran Xie, Wenhan Yang, Mingqiang Wei, and Jing Qin. 2022. UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning. arXiv preprint arXiv:2205.01871 (2022).
[46]
Dong Yang and Jian Sun. 2018. Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In Proceedings of the european conference on computer vision (ECCV). 702–717.
[47]
Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. 2022. Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2037–2046.
[48]
Yang You, Cewu Lu, Weiming Wang, and Chi-Keung Tang. 2018. Relative CNN-RNN: Learning relative atmospheric visibility from images. IEEE Transactions on Image Processing 28, 1 (2018), 45–55.
[49]
Yitong Zheng, Jia Su, Shun Zhang, Mingliang Tao, and Ling Wang. 2022. Dehaze-AGGAN: Unpaired remote sensing image dehazing using enhanced attention-guide generative adversarial networks. IEEE Transactions on Geoscience and Remote Sensing 60 (2022), 1–13.
[50]
Qingsong Zhu, Jiaming Mai, and Ling Shao. 2015. A fast single image haze removal algorithm using color attenuation prior. IEEE transactions on image processing 24, 11 (2015), 3522–3533.

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  1. From Global to Local: An Adaptive Environmental Illumination Estimation for Non-uniform Scattering

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    cover image ACM Conferences
    MMAsia '23: Proceedings of the 5th ACM International Conference on Multimedia in Asia
    December 2023
    745 pages
    ISBN:9798400702051
    DOI:10.1145/3595916
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 01 January 2024

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    Author Tags

    1. atmospheric scattering model
    2. global atmospheric light
    3. local environment illumination
    4. non-uniform scattering removal

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    • Research-article
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    Funding Sources

    • the ?Pioneer? and ?Leading Goose? R&D Program of Zhejiang
    • the Fundamental Research Funds for the Central Universities
    • the National Natural Science Foundation of China

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    MMAsia '23
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    MMAsia '23: ACM Multimedia Asia
    December 6 - 8, 2023
    Tainan, Taiwan

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    Overall Acceptance Rate 59 of 204 submissions, 29%

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