Realistic Nighttime Haze Image Generation with Glow Effect
Pages 96 - 101
Abstract
Abstract: Haze rendering aims to generate realistic nighttime haze images from clear images. The results can be applied to various practical applications, such as nighttime image dehazing algorithms, game scene rendering, shooting filters, etc. We investigate two smaller but challenging problems in nighttime haze rendering, namely 1) how to accurately estimate the transmission map and air light from clear and haze images, respectively, in the absence of paired datasets? 2) How to render the characteristics of a realistic nighttime haze image: glow effect? For this purpose, we propose an unsupervised nighttime haze image rendering method called NHRM (Nighttime Haze Rendering Module). Precisely, the NHRM consists of three modules: 1) an illumination retention module based on Retinex theory. 2) a transmission map estimation network using multi-scale feature fusion and attention mechanism. 3) a glow generation module using tone mapping and convolution bloom technique. Compared with existing haze generation methods, NHRM can render realistic and controllable nighttime haze images by simulating the main features of nighttime haze scenes in an unsupervised manner. Numerous experimental results show that our method outperforms existing rendering methods with better visual effects and quantitative metrics.
References
[1]
Li, B., Lin, Y., Bai, J., Hu, P., Lv, J. and Peng, X. Unsupervised Neural Rendering for Image Hazing. IEEE Transactions on Image Processing (2022).
[2]
Liu, D. and Klette, R. Fog effect for photography using stereo vision. The Visual Computer, 32, 1 (2016), 99-109.
[3]
Zhao, F., Zeng, M., Jiang, B. and Liu, X. Render synthetic fog into interior and exterior photographs. City, 2013.
[4]
Nayar, S. K. and Narasimhan, S. G. Vision in bad weather. IEEE, City, 1999.
[5]
Li, Y., Tan, R. T. and Brown, M. S. Nighttime haze removal with glow and multiple light colors. City, 2015.
[6]
Zhang, J., Cao, Y. and Wang, Z. Nighttime haze removal based on a new imaging model. IEEE, City, 2014.
[7]
Li, X., Kou, K. and Zhao, B. Weather GAN: Multi-domain weather translation using generative adversarial networks. arXiv preprint arXiv:2103.05422 (2021).
[8]
Ye, T., Chen, S., Liu, Y., Ye, Y., Chen, E. and Li, Y. Underwater Light Field Retention: Neural Rendering for Underwater Imaging. City, 2022.
[9]
Lei, N., Guo, Y., An, D., Qi, X., Luo, Z., Yau, S.-T. and Gu, X. Mode collapse and regularity of optimal transportation maps. arXiv preprint arXiv:1902.02934 (2019).
[10]
Karras, T., Laine, S. and Aila, T. A style-based generator architecture for generative adversarial networks. City, 2019.
[11]
Zhu, J.-Y., Park, T., Isola, P. and Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. City, 2017.
[12]
Isola, P., Zhu, J.-Y., Zhou, T. and Efros, A. A. Image-to-image translation with conditional adversarial networks. City, 2017.
[13]
Bodla, N., Hua, G. and Chellappa, R. Semi-supervised FusedGAN for conditional image generation. City, 2018.
[14]
Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W. and Wang, Z. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28, 1 (2018), 492-505.
[15]
Land, E. H. and McCann, J. J. Lightness and retinex theory. Josa, 61, 1 (1971), 1-11.
[16]
Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W. and Jiang, J. URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement. City, 2022.
[17]
Wei, C., Wang, W., Yang, W. and Liu, J. Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 (2018).
[18]
Lei, X., Xie, J., Jiang, Z., Mai, W., Gong, Z., Lu, C., Lu, L. and Shan, Z. KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image. arXiv preprint arXiv:2207.09210 (2022).
[19]
Silberman, N., Hoiem, D., Kohli, P. and Fergus, R. Indoor segmentation and support inference from rgbd images. Springer, City, 2012.
[20]
Geiger, A., Lenz, P., Stiller, C. and Urtasun, R. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32, 11 (2013), 1231-1237.
[21]
Qin, J., Huang, Y. and Wen, W. Multi-scale feature fusion residual network for single image super-resolution. Neurocomputing, 379 (2020), 334-342.
[22]
Woo, S., Park, J., Lee, J.-Y. and Kweon, I. S. Cbam: Convolutional block attention module. City, 2018.
[23]
Debevec, P. and Gibson, S. A tone mapping algorithm for high contrast images. City, 2002.
[24]
Vogel, R. Adding Bloom to High-Dynamic-Range Tone Mapping (2021).
[25]
Johnson, J., Alahi, A. and Fei-Fei, L. Perceptual losses for real-time style transfer and super-resolution. Springer, City, 2016.
[26]
Simonyan, K. and Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[27]
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. and Hochreiter, S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30 (2017).
Index Terms
- Realistic Nighttime Haze Image Generation with Glow Effect
Recommendations
Nighttime Haze Removal with Glow and Multiple Light Colors
ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)This paper focuses on dehazing nighttime images. Most existing dehazing methods use models that are formulated to describe haze in daytime. Daytime models assume a single uniform light color attributed to a light source not directly visible in the ...
Nighttime Haze Removal with Glow Decomposition Using GAN
Pattern RecognitionAbstractIn this paper, we investigate the problem of a single image haze removal in the nighttime. Glow effect is inherently existing in nighttime scenes due to multiple light sources with various colors and prominent glows. As the glow obscures the color ...
From depth-aware haze generation to real-world haze removal
AbstractFor deep learning-based single image dehazing works, their performances seriously depend on the designed models and training dataset. Existing state-of-the-art methods focus on the design of novel dehazing models or the improvement of training ...
Comments
Information & Contributors
Information
Published In
December 2022
365 pages
ISBN:9781450398039
DOI:10.1145/3579895
Copyright © 2022 ACM.
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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 April 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICNCC 2022
ICNCC 2022: 2022 The 11th International Conference on Networks, Communication and Computing
December 9 - 11, 2022
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 40Total Downloads
- Downloads (Last 12 months)28
- Downloads (Last 6 weeks)0
Reflects downloads up to 18 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format