Imaging through Turbulence

This course focuses on the principles of imaging through turbulent media, particularly in the context of atmospheric turbulence and optical systems. Turbulence in the atmosphere, such as air currents, temperature gradients, and humidity variations, causes wavefront distortions that significantly degrade the quality of optical images.

• Introduction to Imaging Through Turbulence: Understanding atmospheric turbulence, scintillation, and beam wandering,
• Physical Mechanisms of Optical Turbulence: Causes of turbulence in the atmosphere and how it affects light propagation,
• Impact on Imaging Systems: How turbulence degrades image quality, contrast, and resolution,
• Characterization of Turbulence: Kolmogorov spectrum, frozen-flow hypothesis, and turbulence parameters,
• Adaptive Optics for Turbulence Mitigation: Overview of adaptive optics systems, wavefront sensing, and wavefront correction,
• Imaging Techniques for Turbulence Compensation: Phase conjugation, multi-aperture systems, and digital image restoration,
• Turbulence Mitigation in Free-Space Optics (FSO): How beam propagation and modulation schemes are affected by turbulence in FSO systems,
• Practical Applications: Astronomical imaging, military reconnaissance, remote sensing, and communication systems,
• Advances in Imaging Through Turbulence: Use of machine learning, deep learning, and AI-based restoration techniques for real-time turbulence compensation

• Optical/EO/IR system engineers working on imaging performance, stabilization, and image quality in outdoor environments

• Free-space optics (FSO) and photonics engineers interested in turbulence effects on propagation and imaging

• Remote sensing and imaging practitioners (surveillance, long-range observation, tracking, astronomy, environmental monitoring)

• Researchers and graduate students in optics/photonics, imaging science, electromagnetics, and signal processing

• Adaptive optics and wavefront sensing users seeking a practical understanding of turbulence characterization and correction

• Image processing / AI engineers developing turbulence-robust enhancement, restoration, and real-time compensation methods

• System designers and R&D teams evaluating mitigation approaches (optical, algorithmic, or hybrid) for degraded imagery