Automatic parameter estimation for atmospheric turbulence mitigation techniques

Stephen Kozacik; Aaron Paolini; Eric Kelmelis

doi:10.1117/12.2177358

12 May 2015 Automatic parameter estimation for atmospheric turbulence mitigation techniques

Stephen Kozacik, Aaron Paolini, Eric Kelmelis

Proceedings Volume 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI; 94520C (2015) https://doi.org/10.1117/12.2177358
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Several image processing techniques for turbulence mitigation have been shown to be effective under a wide range of long-range capture conditions; however, complex, dynamic scenes have often required manual interaction with the algorithm’s underlying parameters to achieve optimal results. While this level of interaction is sustainable in some workflows, in-field determination of ideal processing parameters greatly diminishes usefulness for many operators. Additionally, some use cases, such as those that rely on unmanned collection, lack human-in-the-loop usage. To address this shortcoming, we have extended a well-known turbulence mitigation algorithm based on bispectral averaging with a number of techniques to greatly reduce (and often eliminate) the need for operator interaction. Automations were made in the areas of turbulence strength estimation (Fried’s parameter), as well as the determination of optimal local averaging windows to balance turbulence mitigation and the preservation of dynamic scene content (non-turbulent motions). These modifications deliver a level of enhancement quality that approaches that of manual interaction, without the need for operator interaction. As a consequence, the range of operational scenarios where this technology is of benefit has been significantly expanded.

Citation Download Citation

Stephen Kozacik, Aaron Paolini, and Eric Kelmelis "Automatic parameter estimation for atmospheric turbulence mitigation techniques", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 94520C (12 May 2015); https://doi.org/10.1117/12.2177358

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available