  The License Plate Recognition (LPR) field has made impressive advances in the
last decade due to novel deep learning approaches combined with the increased
availability of training data. However, it still has some open issues,
especially when the data come from low-resolution (LR) and low-quality
images/videos, as in surveillance systems. This work focuses on license plate
(LP) reconstruction in LR and low-quality images. We present a Single-Image
Super-Resolution (SISR) approach that extends the attention/transformer module
concept by exploiting the capabilities of PixelShuffle layers and that has an
improved loss function based on LPR predictions. For training the proposed
architecture, we use synthetic images generated by applying heavy Gaussian
noise in terms of Structural Similarity Index Measure (SSIM) to the original
high-resolution (HR) images. In our experiments, the proposed method
outperformed the baselines both quantitatively and qualitatively. The datasets
we created for this work are publicly available to the research community at
https://github.com/valfride/lpr-rsr/

Computer Vision and Pattern Recognition