An appraisal of suitable evaluation methods for turbulence mitigation algorithms

Claudia S. Huebner

doi:10.1117/12.2638815

26 October 2022 An appraisal of suitable evaluation methods for turbulence mitigation algorithms

Claudia S. Huebner

Proceedings Volume 12266, Environmental Effects on Light Propagation and Adaptive Systems V; 122660G (2022) https://doi.org/10.1117/12.2638815
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

In order to mitigate atmospheric turbulence effects such as increased blur, reduced contrast, and image motion as well as geometric deformations, a wide variety of reconstruction techniques has been developed. Such techniques have proven reasonably successful in mitigating one or several turbulence effects but frequently at the cost of introducing unwanted artefacts such as ringing or noise amplification, depending on the algorithm's properties. The application of image quality metrics (IQMs) as a means of comparing the results of various reconstruction algorithms as objectively as possible is a widely used practice. However, added noise and artefacts affect IQMs which rely on information like high frequency components, disproportionately, since noisy results are invariably interpreted as "higher quality". The underlying goal of this article is to define a methodology for comparing the performance of structurally differing algorithms by a combination of select quality metrics. As different metrics will likely yield different ratings for the same algorithm's performance, a combination of suitable metrics is proposed. Therefore the main focus here is foremost on a survey of current methods for assessing image quality in general and on appraising their suitability for evaluating the quality of images processed by turbulence mitigation algorithms in particular.

Conference Presentation

Citation Download Citation

Claudia S. Huebner "An appraisal of suitable evaluation methods for turbulence mitigation algorithms", Proc. SPIE 12266, Environmental Effects on Light Propagation and Adaptive Systems V, 122660G (26 October 2022); https://doi.org/10.1117/12.2638815

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