A survey on facial image deblurring

What is it about?

Blurry face images can make tasks like face recognition harder. This paper reviews methods to restore clear faces from blurry photos. Older techniques use mathematical models to reverse blur but struggle with real-world complexity. Newer AI-based methods learn from examples and use face-specific features (like eye/nose structure) to guide the process, improving accuracy. However, challenges remain—like handling different blur types (motion, focus) and real-world variations. The paper compares methods, datasets, and metrics, highlighting that AI models are faster and better but still need improvements for diverse, real-life scenarios. Future research aims to make these tools more robust and practical for everyday use.

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Why is it important?

This paper uniquely focuses on facial deblurring surveys. It bridges gaps by comparing classic vs. AI methods, highlights how face-specific features (like structure) boost accuracy, and provides clear benchmarks. This targeted guide helps researchers/practitioners choose the right tools, accelerating real-world solutions for security, photography, and AI systems.