VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
This work addresses a domain-specific problem for traffic surveillance systems by enhancing license plate recognition accuracy through incremental improvements in image restoration methods.
The paper tackled the problem of license plate image degradation in traffic surveillance by proposing a Vertical Residual Autoencoder (VRAE) for denoising and deblurring, resulting in improvements such as a 20% increase in PSNR, 50% reduction in NMSE, and 1% enhancement in SSIM compared to baseline autoencoders.
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.