Abstract
The combination of Fourier Optics and Artificial Intelligence has driven significant advances in image processing and modeling of optical systems, with the UNet architecture being the main protagonist. However, the DeepLabV3+ network has recently shown promising performance in detecting transfer opens. In this study, we investigate the effectiveness of DeepLabV3+ in identifying transfer apertures in light propagation models and compare its performance with that of UNet. The results reveal that DeepLabV3+ outperforms UNet in terms of accuracy and robustness in identifying transfer apertures, even in the presence of noise and aperture shape variations.
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