License Plate Restoration System

Meng Chen Luo; Chia Yu Lin

doi:10.1109/ICCE-Taiwan58799.2023.10226883

License Plate Restoration System

Meng Chen Luo, Chia Yu Lin

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The advent of intelligent transportation systems has led to the direct recognition of license plates by license plate recognition systems. However, the system is affected by environmental factors, such as underexposure, resulting in over-dark images. In this paper, we propose a two-stage restoration system to address these issues. In the first stage, we use image processing to enhance the license plate, and in the second stage, we employ a deep learning model to further repair the license plate. Our approach has greatly improved the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, increasing by 3.8094 and 0.0137.

Original language	English
Title of host publication	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	333-334
Number of pages	2
ISBN (Electronic)	9798350324174
DOIs	https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226883
State	Published - 2023
Event	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan Duration: 17 Jul 2023 → 19 Jul 2023

Publication series

Name	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/Territory	Taiwan
City	Pingtung
Period	17/07/23 → 19/07/23

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10.1109/ICCE-Taiwan58799.2023.10226883

Cite this

Luo, M. C., & Lin, C. Y. (2023). License Plate Restoration System. In 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings (pp. 333-334). (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226883

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title = "License Plate Restoration System",

abstract = "The advent of intelligent transportation systems has led to the direct recognition of license plates by license plate recognition systems. However, the system is affected by environmental factors, such as underexposure, resulting in over-dark images. In this paper, we propose a two-stage restoration system to address these issues. In the first stage, we use image processing to enhance the license plate, and in the second stage, we employ a deep learning model to further repair the license plate. Our approach has greatly improved the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, increasing by 3.8094 and 0.0137.",

author = "Luo, {Meng Chen} and Lin, {Chia Yu}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",

year = "2023",

doi = "10.1109/ICCE-Taiwan58799.2023.10226883",

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series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",

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}

Luo, MC & Lin, CY 2023, License Plate Restoration System. in 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 333-334, 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023, Pingtung, Taiwan, 17/07/23. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226883

License Plate Restoration System. / Luo, Meng Chen; Lin, Chia Yu.
2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 333-334 (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Luo, Meng Chen

AU - Lin, Chia Yu

PY - 2023

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N2 - The advent of intelligent transportation systems has led to the direct recognition of license plates by license plate recognition systems. However, the system is affected by environmental factors, such as underexposure, resulting in over-dark images. In this paper, we propose a two-stage restoration system to address these issues. In the first stage, we use image processing to enhance the license plate, and in the second stage, we employ a deep learning model to further repair the license plate. Our approach has greatly improved the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, increasing by 3.8094 and 0.0137.

AB - The advent of intelligent transportation systems has led to the direct recognition of license plates by license plate recognition systems. However, the system is affected by environmental factors, such as underexposure, resulting in over-dark images. In this paper, we propose a two-stage restoration system to address these issues. In the first stage, we use image processing to enhance the license plate, and in the second stage, we employ a deep learning model to further repair the license plate. Our approach has greatly improved the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, increasing by 3.8094 and 0.0137.

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AN - SCOPUS:85174964082

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BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

Y2 - 17 July 2023 through 19 July 2023

ER -

License Plate Restoration System

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