Part of the book series:Communications in Computer and Information Science ((CCIS,volume 1491))
Included in the following conference series:
882Accesses
Abstract
Image dehazing is an important pre-processing for computer vision systems. Modern dehazing techniques are based deep learning and training data. Typical datasets are constructed with depth camera for indoor scene. However, it is challenging to construct outdoor dataset because the limitation of depth information devices. This paper presents a novel construction approach for outdoor dehazing dataset. In our approach, we propose to adopt realistic rendering engine to generate pairs of both hazy and clear images for outdoor scene. Initial experiments on typical dehazing networks confirm the proposed idea and construction method.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 17159
- Price includes VAT (Japan)
- Softcover Book
- JPY 21449
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012).https://doi.org/10.1007/978-3-642-33715-4_54
Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process.28(1), 492–505 (2019)
Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell.38(10), 2024–2039 (2015)
McCartney, E.J.: Optics of the Atmosphere: Scattering by Molecules and Particles. New York (1976)
Xing, L., Yang, L.: Image restoration using prior information physics model. In: 2011 4th International Congress on Image and Signal Processing, vol. 2, pp. 786–789. IEEE (2011)
Fattal, R.: Single image dehazing. ACM Trans. Graph.27(3), 1–9 (2008)
Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), vol. 1, pp. 598–605. IEEE (2000)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell.33(12), 2341–2353 (2010)
Cai, B., Xiangmin, X., Jia, K., Qing, C., Tao, D.: Dehazenet: An end-to-end system for single image haze removal. IEEE Trans. Image Pro-cess.25(11), 5187–5198 (2016)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: Proceedings of the IEEE international conference on comput-er vision, pp. 4770–4778 (2017)
Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3194–3203 (2018)
Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: Ffa-net: Feature fusion attention network for single image dehazing. Proc. AAAI Conf. Artif. Intell.34, 11908–11915 (2020)
Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014).https://doi.org/10.1007/978-3-319-11752-2_3
FlightGear: Flightgear flight simulator – sophisticated, professional, open-source.https://www.flightgear.org/. Accessed 29 June 2021
Autoscreencap download—sourceforge.net.https://sourceforge.net/projects/autoscreencap/. Accessed 29 June 2021
Chen, D.: Gated context aggregation network for image dehazing and deraining. In: 2019 IEEE Winter Conference on Applications Of Computer Vision (WACV), pp. 1375–1383. IEEE (2019)
Author information
Authors and Affiliations
School of Computer Science, Wuhan University, Wuhan, China
Shizhen Yang, Fazhi He, Jiacheng Gao & Jinkun Luo
- Shizhen Yang
You can also search for this author inPubMed Google Scholar
- Fazhi He
You can also search for this author inPubMed Google Scholar
- Jiacheng Gao
You can also search for this author inPubMed Google Scholar
- Jinkun Luo
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toFazhi He.
Editor information
Editors and Affiliations
Shandong University, Jinan, China
Yuqing Sun
Fudan University, Shanghai, China
Tun Lu
Hunan University of Science and Technology, Xiangtan, China
Buqing Cao
Tongji University, Shanghai, China
Hongfei Fan
Guangdong University of Technology, Guangzhou, China
Dongning Liu
University of Warwick, Coventry, UK
Bowen Du
University of Shanghai for Science and Technology, Shanghai, China
Liping Gao
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, S., He, F., Gao, J., Luo, J. (2022). A Novel Construction Approach for Dehazing Dataset Based on Realistic Rendering Engine. In: Sun, Y.,et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_17
Download citation
Published:
Publisher Name:Springer, Singapore
Print ISBN:978-981-19-4545-8
Online ISBN:978-981-19-4546-5
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
