A modified single image dehazing method for autonomous driving vision system-Reference-Cited by-同舟云学术

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A modified single image dehazing method for autonomous driving vision system

Published:2023-08-25 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Kim Wong Yoke,Hum Yan Chai^ORCID,Tee Yee Kai,Yap Wun-She,Mokayed Haman,Lai Khin Wee

Funder

Universiti Tunku Abdul Rahman

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16547-8.pdf

Reference42 articles.

1. Ancuti C, Ancuti CO, De Vleeschouwer C (2016) D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. Proceedings - international conference on image processing, pp 2226–2230. https://doi.org/10.1109/ICIP.2016.7532754

2. Ancuti CO, Ancuti C, Hermans C, Bekaert P (2011) A fast semi-inverse approach to detect and remove the haze from a single image. Lecture notes in computer science, 6493 LNCS(PART 2), pp 501–514. https://doi.org/10.1007/978-3-642-19309-5_39

3. Ancuti CO, Ancuti C, Timofte R, De Vleeschouwer C (2018a) O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. IEEE computer society conference on computer vision and pattern recognition workshops, pp 867–875. https://doi.org/10.1109/CVPRW.2018.00119

4. Ancuti CO, Ancuti C, Timofte R, De Vleeschouwer C (2018b) I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. IEEE computer society conference on computer vision and pattern recognition workshops, pp 1581–1590. https://doi.org/10.1109/CVPRW.2018.00211

5. Breiman L (2001) Random forest. Mach Learn 45:5–32

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3. Enhanced Deep Dehazing for Haze Removal in License Plates;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03

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