An Enhanced Variational AutoEncoder Approach for the Purpose of Deblurring Bangla License Plate Images

Automated License Plate Detection and Recognition (ALPDR) is a well-studied area of computer vision and a crucial activity in a variety of applications, including surveillance, law enforcement, and traffic management. Such a system plays a crucial role in the investigation of vehicle-related offensive activities. When an input image or video frame travels through an ALPDR system for license plate detection, the detected license plate is frequently blurry due to the fast motion of the vehicle or low-resolution input. Images of license plates that are blurred or distorted can reduce the accuracy of ALPDR systems. In this paper, a novel Variational AutoEncoder(VAE) architecture is proposed for deblurring license plates. In addition, a dataset of obscured license plate images and corresponding ground truth images is proposed and used to train the novel VAE model. This dataset comprises 3788 image pairs, in which the train, test, and validation set contains 2841, 568, and 379 pairs of images respectively. Upon completion of the training process, the model undergoes an evaluation procedure utilizing the validation set, where it achieved an SSIM value of 0.934 and a PSNR value of 32.41. In order to assess the efficacy of our proposed VAE model, a comparison with contemporary deblurring techniques is pre-sented in the results section. In terms of both quantitative metrics and the visual quality of the deblurred images, the experimental results indicate that our proposed method outperforms the other state-of-the-art deblurring methods. Therefore, it enhances the precision and dependability of an ALPDR system.