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| 題 名 | KangNet: Compact Deblurring Model with GAN-Based Blur Translation for License Plate Recognition |
|---|---|
| 作 者 | Kang, Jia-hao; Tung, Tzu-chia; Chen, Ping-han; Chu, Yu-chin; Chen, Yu-jen; Fuh, Chiou-shann; Chou, Yung-chien; Liao, Yu-tang; Yang, Chung-ming; Huang, Shu-ru; | 書刊名 | Journal of Information Science and Engineering |
| 卷 期 | 42:1 2026.01[民115.01] |
| 頁 次 | 頁213-226 |
| 分類號 | 312.831 |
| 關鍵詞 | License plate; Image deblurring; Deep learning; Lightweight architecture; Image restoration; |
| 語 文 | 英文(English) |
| DOI | 10.6688/JISE.202601_42(1).0012 |
| 英文摘要 | In the realm of technological law enforcement, the challenge of blurred vehicle license plate images captured by surveillance cameras often impedes the efficient identification and penalization of traffic violations. This paper addresses the critical need for an effective deblurring solution that enhances the readability of license plates under such circumstances. The current State-Of-The-Art (SOTA) methods in image deblurring demonstrate limitations in handling the diverse and unpredictable conditions encountered in realworld scenarios. Our study modified the NAFNet model architecture, making it more compact and improving its execution speed, which is particularly beneficial for real-time image restoration. We have also incorporated domain adaptation techniques to enhance the model’s ability to handle real-world blur scenarios. These techniques allowed the model to bridge the domain gap between synthetic blur data and real-world blur images. |
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