Multi-Frame Super-Resolution License Plate Recognition

Multi-Frame Super-Resolution License Plate Recognition

An ongoing research project for the ICPR 2026 Competition that addresses the challenging problem of reading low-resolution license plates using multi-frame super-resolution (MFSR) techniques.

Status: Currently in development phase for ICPR 2026 Competition

Project Overview

This project tackles the challenging problem of license plate recognition in low-resolution scenarios by leveraging multi-frame super-resolution techniques. The system is being developed to handle real-world conditions where traditional single-frame approaches fail.

Development Phase: The project is currently under active development. Detailed methodology, results, and findings will be shared upon completion and after the ICPR 2026 Competition concludes.

Core Technologies

• Deep Learning - Neural network-based super-resolution and detection
• Computer Vision - Multi-frame image processing and alignment
• Super-Resolution - Temporal information fusion for quality enhancement
• YOLO - Real-time license plate detection
• PyTorch - Deep learning framework

Competition Information

ICPR 2026 - International Conference on Pattern Recognition

This project is being developed for the ICPR 2026 competition track on low-resolution license plate recognition.

Note

This project is currently in the development phase. Comprehensive details, methodology, experimental results, and performance metrics will be published upon project completion and after the ICPR 2026 Competition concludes.