VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
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arXiv
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| Format: | Preprint |
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2025
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| author | Nguyen, Cuong Tran, Dung T. Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong |
| author_facet | Nguyen, Cuong Tran, Dung T. Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong |
| contents | In real-world traffic surveillance, vehicle images captured under adverse weather, poor lighting, or high-speed motion often suffer from severe noise and blur. Such degradations significantly reduce the accuracy of license plate recognition systems, especially when the plate occupies only a small region within the full vehicle image. Restoring these degraded images a fast realtime manner is thus a crucial pre-processing step to enhance recognition performance. In this work, we propose a Vertical Residual Autoencoder (VRAE) architecture designed for the image enhancement task in traffic surveillance. The method incorporates an enhancement strategy that employs an auxiliary block, which injects input-aware features at each encoding stage to guide the representation learning process, enabling better general information preservation throughout the network compared to conventional autoencoders. Experiments on a vehicle image dataset with visible license plates demonstrate that our method consistently outperforms Autoencoder (AE), Generative Adversarial Network (GAN), and Flow-Based (FB) approaches. Compared with AE at the same depth, it improves PSNR by about 20%, reduces NMSE by around 50%, and enhances SSIM by 1%, while requiring only a marginal increase of roughly 1% in parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring Nguyen, Cuong Tran, Dung T. Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong Computer Vision and Pattern Recognition In real-world traffic surveillance, vehicle images captured under adverse weather, poor lighting, or high-speed motion often suffer from severe noise and blur. Such degradations significantly reduce the accuracy of license plate recognition systems, especially when the plate occupies only a small region within the full vehicle image. Restoring these degraded images a fast realtime manner is thus a crucial pre-processing step to enhance recognition performance. In this work, we propose a Vertical Residual Autoencoder (VRAE) architecture designed for the image enhancement task in traffic surveillance. The method incorporates an enhancement strategy that employs an auxiliary block, which injects input-aware features at each encoding stage to guide the representation learning process, enabling better general information preservation throughout the network compared to conventional autoencoders. Experiments on a vehicle image dataset with visible license plates demonstrate that our method consistently outperforms Autoencoder (AE), Generative Adversarial Network (GAN), and Flow-Based (FB) approaches. Compared with AE at the same depth, it improves PSNR by about 20%, reduces NMSE by around 50%, and enhances SSIM by 1%, while requiring only a marginal increase of roughly 1% in parameters. |
| title | VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.08392 |