Methodology for the empirical representation of image processing functions in EO/IR sensor system models and simulations

Duncan L. Hickman

doi:10.1117/12.2657037

2 November 2022 Methodology for the empirical representation of image processing functions in EO/IR sensor system models and simulations

Duncan L. Hickman

Author Affiliations +

Proceedings Volume 12271, Electro-optical and Infrared Systems: Technology and Applications XIX; 122710A (2022) https://doi.org/10.1117/12.2657037
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Image processing design plays an important role in the overall performance of modern EO/IR cameras. Incorporating these functions into models and simulators presents a major design challenge, particularly for those cases where complex processing functions are required, such as those found in autonomous ATD/R systems. An established technique involves the use of image-based simulations which process pre-recorded imagery using representative image processing algorithms. However, such an approach requires extensive run times and a large volume of image data, both of which can be prohibitive. An alternative approach is presented here whereby a limited number of images are processed and then used to generate statistically based performance transfer functions utilising an appropriate interpolation scheme. These transfer functions are then used to represent the output response of the processing chain when the received imagery is subjected to different levels of degradations such as distortion and blurring. Such transfer functions can then be stored in multidimensional look-up tables which can be rapidly accessed by a system-level Monte Carlo performance simulation. The ability to represent and extract the performance-related transfer functions is dependent upon the image quality metrics, and the accuracy of the corresponding parametric model requires careful consideration of the model validation. An example simulation is presented based on an autonomous ATD/R sensor system mounted on an airborne platform. The importance of validation is demonstrated, and the increased run-time benefits are described. The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.

Conference Presentation

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Duncan L. Hickman "Methodology for the empirical representation of image processing functions in EO/IR sensor system models and simulations", Proc. SPIE 12271, Electro-optical and Infrared Systems: Technology and Applications XIX, 122710A (2 November 2022); https://doi.org/10.1117/12.2657037

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