Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario

Research Article
Open access

Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario

Qiyuan Yang 1*
  • 1 School of Software, Yunnan University    
  • *corresponding author qqq1277@mail.ynu.edu.cn
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220538
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

In the field of automatic driving, clear image is one of the important prerequisites for the decision-making system relying on computer vision to make correct decisions. Rainy days and low illumination environment are two common scenes that seriously affect image quality. Researchers have proposed many effective methods for these two research fields. However, few applications have been studied in the field of automatic driving comprehensively. To solve the problem, a low illumination image enhancement method in rainy days is proposed, which combines the two scenes and can be applied to the field of automatic driving. This algorithm takes good advantage of the fact that raindrops cannot appear in the same frame at different times to eliminate the noise of raindrops in the image .And its main process is to fuse continuous image frames by maximum method through multi-scale Gaussian filtering. Thus, the fused image is used as the illumination map and the original image to be denoised is used as the input of Multi-Scale Retinex(MSR) method.Finally, the reflection image which can be directly applied in automatic driving is obtained.

Keywords:

Retinex., low light, rain removal

Yang,Q. (2023). Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario. Applied and Computational Engineering,2,862-867.
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References

[1]. Navneet D and Bill T. “Histograms of oriented gradients for human detection”. In:2005IEEE computer society conference on computer vision and pattern recognition (CVPR’05). Vol. 1.Ieee. 2005, pp. 886–893.

[2]. Liu Y et al. "Upsampling Matters for Road Marking Segmentation of Autonomous Driving." IFAC-PapersOnLine 53.5 (2020): 232-237.

[3]. Yang W et al. "Deep joint rain detection and removal from a single image." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

[4]. Li S et al. “Single image deraining: A comprehensive benchmark analysis”. In:Proceedingsof the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp. 3838–3847.

[5]. Wang H et al. "A survey on rain removal from video and single image." arXiv preprint arXiv:1909.08326 (2019)

[6]. Kang L, Lin C, and Fu Y. "Automatic single-image-based rain streaks removal via image decomposition." IEEE transactions on image processing 21.4 (2011): 1742-1755.

[7]. Wei, W et al. "Semi-supervised transfer learning for image rain removal." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.

[8]. Zhang X et al. "Rain removal in video by combining temporal and chromatic properties." 2006 IEEE international conference on multimedia and expo. IEEE, 2006.

[9]. Tang L et al. "Improved retinex image enhancement algorithm." Procedia Environmental Sciences 11 (2011): 208-212.

[10]. Wei C et al. "Deep retinex decomposition for low-light enhancement." arXiv preprint arXiv:1808.04560 (2018).

[11]. Guo X, Yu L, and Haibin Ling. "LIME: Low-light image enhancement via illumination map estimation." IEEE Transactions on image processing 26.2 (2016): 982-993.

[12]. Kin Gwn L, Akintayo A, and Sarkar S. "LLNet: A deep autoencoder approach to natural low-light image enhancement." Pattern Recognition 61 (2017): 650-662.

[13]. Kshitiz G, and N S K. "Detection and removal of rain from videos." Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.. Vol. 1. IEEE, 2004.

[14]. Kenneth V B, and Chuang C. "A new model for the equilibrium shape of raindrops." Journal of Atmospheric Sciences 44.11 (1987): 1509-1524.

[15]. Sonia S, and Werman M. "Simulation of rain in videos." Texture Workshop, ICCV. Vol. 2. 2003.

[16]. A. K. T, and Mukhopadhyay S . "Video post processing: low-latency spatiotemporal approach for detection and removal of rain." IET image processing 6.2 (2012): 181-196.

[17]. Edwin H L. "The retinex theory of color vision." Scientific american 237.6 (1977): 108-129.

[18]. Liang S, et al. "Msr-net: Low-light image enhancement using deep convolutional network." arXiv preprint arXiv:1711.02488 (2017).

Cite this article

Yang,Q. (2023). Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario. Applied and Computational Engineering,2,862-867.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Navneet D and Bill T. “Histograms of oriented gradients for human detection”. In:2005IEEE computer society conference on computer vision and pattern recognition (CVPR’05). Vol. 1.Ieee. 2005, pp. 886–893.

[2]. Liu Y et al. "Upsampling Matters for Road Marking Segmentation of Autonomous Driving." IFAC-PapersOnLine 53.5 (2020): 232-237.

[3]. Yang W et al. "Deep joint rain detection and removal from a single image." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

[4]. Li S et al. “Single image deraining: A comprehensive benchmark analysis”. In:Proceedingsof the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp. 3838–3847.

[5]. Wang H et al. "A survey on rain removal from video and single image." arXiv preprint arXiv:1909.08326 (2019)

[6]. Kang L, Lin C, and Fu Y. "Automatic single-image-based rain streaks removal via image decomposition." IEEE transactions on image processing 21.4 (2011): 1742-1755.

[7]. Wei, W et al. "Semi-supervised transfer learning for image rain removal." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.

[8]. Zhang X et al. "Rain removal in video by combining temporal and chromatic properties." 2006 IEEE international conference on multimedia and expo. IEEE, 2006.

[9]. Tang L et al. "Improved retinex image enhancement algorithm." Procedia Environmental Sciences 11 (2011): 208-212.

[10]. Wei C et al. "Deep retinex decomposition for low-light enhancement." arXiv preprint arXiv:1808.04560 (2018).

[11]. Guo X, Yu L, and Haibin Ling. "LIME: Low-light image enhancement via illumination map estimation." IEEE Transactions on image processing 26.2 (2016): 982-993.

[12]. Kin Gwn L, Akintayo A, and Sarkar S. "LLNet: A deep autoencoder approach to natural low-light image enhancement." Pattern Recognition 61 (2017): 650-662.

[13]. Kshitiz G, and N S K. "Detection and removal of rain from videos." Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.. Vol. 1. IEEE, 2004.

[14]. Kenneth V B, and Chuang C. "A new model for the equilibrium shape of raindrops." Journal of Atmospheric Sciences 44.11 (1987): 1509-1524.

[15]. Sonia S, and Werman M. "Simulation of rain in videos." Texture Workshop, ICCV. Vol. 2. 2003.

[16]. A. K. T, and Mukhopadhyay S . "Video post processing: low-latency spatiotemporal approach for detection and removal of rain." IET image processing 6.2 (2012): 181-196.

[17]. Edwin H L. "The retinex theory of color vision." Scientific american 237.6 (1977): 108-129.

[18]. Liang S, et al. "Msr-net: Low-light image enhancement using deep convolutional network." arXiv preprint arXiv:1711.02488 (2017).

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