Improving classification accuracy in post-turbulence-mitigation tasks via perceptual loss optimization

Image distortions caused by atmospheric turbulence have long posed a significant challenge for accurate imaging and observation. The ability to mitigate such distortions, without having to alter the imaging setup, is therefore extremely desirable. In recent years, deep learning based techniques for turbulence mitigation have achieved, and set, state of the art performance. However, we propose that the definition of 'State of the Art', with regards to turbulence mitigation, may not be based on the most appropriate metrics. Unlike applications such as super resolution, or denoising, the goal of turbulence mitigation is not necessarily to provide a high quality, aesthetically pleasing image. The goal, rather, is more likely to be that of improving subsequent processing (i.e. post mitigation) tasks. For example, when performing target detection or classification, it should be the improvement in performance of such post mitigation tasks that measures the quality of a turbulence mitigation model. Literature commonly makes use of image quality metrics for the turbulence mitigation task; however, whilst these metrics may be informative, their use may result in 'sub-optimal' mitigation algorithms being promoted to generate turbulence-mitigated images that achieve high metric scores, but lack appropriate quality for downstream applications. Previous works showed that by training a turbulence mitigation model with classification in mind, the performance of the model could be improved. This paper proposes an improvement on the state of the art model, DATUM, by incorporating perceptual loss into its training. By analysing its loss function, the model is better tuned towards turbulence mitigation, facilitating better post-mitigation performance in a classification task. Furthermore, this paper also presents an investigation into the computational requirements of such models, with the motivation of understanding applicability in the real world. This paper aims to steer turbulence mitigation research in the right direction, by highlighting the importance of downstream tasks as well as real-world deployability.