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

Abstract

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/

Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2022

References in corpus (4)

• Rethinking and Designing a High-performing Automatic License Plate Recognition Approach
• On the Cross-dataset Generalization in License Plate Recognition
• Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach
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Cited by in corpus (7)

• Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
• Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
• License Plate Super-Resolution Using Diffusion Models
• Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark
• Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach
• MF-LPR${}^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
• Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models