Huaizhuo Liu
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.
- 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.Google Scholar
Cross Ref
- Zahra Anvari and Vassilis Athitsos. 2020. Dehaze-GLCGAN: unpaired single image de-hazing via adversarial training. arXiv preprint arXiv:2008.06632 (2020).Google Scholar
- Dana Berman, Shai Avidan, 2016. Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1674–1682.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Trung Minh Bui and Wonha Kim. 2017. Single image dehazing using color ellipsoid prior. IEEE Transactions on Image Processing 27, 2 (2017), 999–1009.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- Xuan Dong, Yi Pang, and Jiangtao Wen. 2010. Fast efficient algorithm for enhancement of low lighting video. In ACM SIGGRAPH 2010 Posters. 1–1.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- Kaiming He, Jian Sun, and Xiaoou Tang. 2012. Guided image filtering. IEEE transactions on pattern analysis and machine intelligence 35, 6 (2012), 1397–1409.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Zheyan Jin, Shiqi Chen, Yueting Chen, Zhihai Xu, and Huajun Feng. 2023. Let Segment Anything Help Image Dehaze. arXiv preprint arXiv:2306.15870 (2023).Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Edwin H Land. 1977. The retinex theory of color vision. Scientific american 237, 6 (1977), 108–129.Google Scholar
- Edwin H Land and John J McCann. 1971. Lightness and retinex theory. Josa 61, 1 (1971), 1–11.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Kresimir Matkovic, László Neumann, Attila Neumann, Thomas Psik, Werner Purgathofer, 2005. Global contrast factor-a new approach to image contrast.. In CAe. 159–167.Google Scholar
- Earl J McCartney. 1976. Optics of the atmosphere: scattering by molecules and particles. New York (1976).Google Scholar
- William Edgar Knowles Middleton. 1957. Vision through the atmosphere. Springer.Google Scholar
- 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.Google ScholarCross Ref
- Ana Belén Petro, Catalina Sbert, and Jean-Michel Morel. 2014. Multiscale retinex. Image Processing On Line (2014), 71–88.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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).Google ScholarCross Ref
- 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.Google ScholarDigital Library
- J Alex Stark. 2000. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on image processing 9, 5 (2000), 889–896.Google ScholarDigital Library
- Sabine E Susstrunk and Stefan Winkler. 2003. Color image quality on the internet. In Internet imaging V, Vol. 5304. SPIE, 118–131.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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).Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
Index Terms
- From Global to Local: An Adaptive Environmental Illumination Estimation for Non-uniform Scattering
Recommendations
Precomputed illuminance composition for real-time global illumination
I3D '16: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and GamesIn this paper we present a new real-time approach for indirect global illumination under dynamic lighting conditions. We use surfels to gather a sampling of the local illumination and propagate the light through the scene using a hierarchy and a set of ...
Comments