VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring

• URL: http://arxiv.org/abs/2509.08392v2
• Date: Thu, 11 Sep 2025 16:45:28 GMT
• Title: VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
• Authors: Cuong Nguyen, Dung T. Tran, Hong Nguyen, Xuan-Vu Phan, Nam-Phong Nguyen,
• Abstract summary: Restoring degraded images a fast realtime manner is thus a crucial pre-processing step to enhance recognition performance.<n>In this work, we propose a Vertical Residual Autoencoder architecture designed for the image enhancement task in traffic surveillance.<n> 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.
• Score: 2.1639459844313564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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