Super-Resolution of License Plate Images Using Attention Modules and
  Sub-Pixel Convolution Layers
Valfride Nascimento Rayson Laroca Jorge de A. Lambert William Robson Schwartz David Menotti
Recent years have seen significant developments in the field of License Plate
Recognition (LPR) through the integration of deep learning techniques and the
increasing availability of training data. Nevertheless, reconstructing license
plates (LPs) from low-resolution (LR) surveillance footage remains challenging.
To address this issue, we introduce a Single-Image Super-Resolution (SISR)
approach that integrates attention and transformer modules to enhance the
detection of structural and textural features in LR images. Our approach
incorporates sub-pixel convolution layers (also known as PixelShuffle) and a
loss function that uses an Optical Character Recognition (OCR) model for
feature extraction. We trained the proposed architecture on synthetic images
created by applying heavy Gaussian noise to high-resolution LP images from two
public datasets, followed by bicubic downsampling. As a result, the generated
images have a Structural Similarity Index Measure (SSIM) of less than 0.10. Our
results show that our approach for reconstructing these low-resolution
synthesized images outperforms existing ones in both quantitative and
qualitative measures. Our code is publicly available at
https://github.com/valfride/lpr-rsr-ext/