Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

  • Valfride Nascimento UFPR
  • Rayson Laroca UFPR
  • Jorge de A. Lambert Polícia Federal
  • William Robson Schwartz UFMG
  • David Menotti UFPR


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 PixelShuftle layers capabilities 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

Palavras-chave: Training, Image recognition, Surveillance, Superresolution, Training data, Computer architecture, Noise measurement
NASCIMENTO, Valfride; LAROCA, Rayson; LAMBERT, Jorge de A.; SCHWARTZ, William Robson; MENOTTI, David. Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 .