Abstract
Optical imaging quality can be severely degraded by system and sample induced aberrations. Existing adaptive optics systems typically rely on iterative search algorithm to correct for aberrations and improve images. This study demonstrates the application of convolutional neural networks to characterise the optical aberration by directly predicting the Zernike coefficients from two to three phase-diverse optical images. We evaluated our network on 600,000 simulated Point Spread Function (PSF) datasets randomly generated within the range of -1 to 1 radians using the first 25 Zernike coefficients. The results show that using only three phase-diverse images captured above, below and at the focal plane with an amplitude of 1 achieves a low RMSE of 0.10 radians on the simulated PSF dataset. Furthermore, this approach directly predicts Zernike modes simulated extended 2D samples, while maintaining a comparable RMSE of 0.15 radians. We demonstrate that this approach is effective using only a single prediction step, or can be iterated a small number of times. This simple and straightforward technique provides rapid and accurate method for predicting the aberration correction using three or less phase-diverse images, paving the way for evaluation on real-world dataset.
Abstract (translated)
光学成像质量可能会受到系统和样品诱导的色差严重降解。现有的自适应光学系统通常依赖于迭代搜索算法来纠正色差并改善图像。本研究展示了将卷积神经网络应用于直接从两到三个相干 diverse光学图像中预测Zernike系数来表征光学色差的应用。我们使用前25个Zernike系数对600,000个随机的点扩散函数（PSF）数据集进行了评估。结果表明，仅使用上面、下面和焦点平面捕获的三个相干 diverse 图像，其幅度为1的模拟 PSF 数据集的 RMSE 低至 0.10 弧度。此外，这种方法还直接预测模拟扩展 2D 样本的 Zernike 模式，同时保持与模拟 PSF 数据集相当 RMSE 值（0.15 弧度）。我们证明了这种方法仅用一个预测步骤即可有效，或者可以进行少量迭代。这种简单而直接的技术为使用三个或更少的相干 diverse 图像预测色差纠正提供了快速且准确的方法，为在现实世界数据集上进行评估铺平了道路。
URL
https://arxiv.org/abs/2404.15231
