Atmospheric Turbulence Mitigation
A transformer-based AI solution for reducing atmospheric turbulence effects in imaging and improving inference quality.
Project Overview
This project focuses on mitigating the effects of atmospheric turbulence in imaging systems, particularly for aerial and satellite-based applications. By leveraging transformer-based deep learning models and adaptive filtering techniques, the system enhances image clarity, reduces distortions, and improves object detection in turbulent conditions.
Features
- Transformer-Based Turbulence Removal: AI-driven deblurring and denoising techniques enhance visual clarity.
- Real-Time Processing: Optimized transformer models for real-time applications.
- Object Detection Improvement: Enhanced object recognition accuracy after turbulence correction.
- Adaptive Filtering: Custom filtering algorithms dynamically adjust to varying turbulence levels.
Challenges & Solutions
- Severe Image Distortions: Implemented transformer-based restoration models.
- High Computational Cost: Applied model compression and hardware acceleration.
- Maintaining Temporal Consistency: Developed sequential processing for stable frame restoration.
Results
The system significantly enhances image quality in turbulent conditions, making it useful for surveillance, astronomy, and remote sensing applications.
Demo & Repository
For implementation details and live demos, visit the GitHub Repository.
AI-enhanced image after turbulence mitigation, improving clarity and detection accuracy.