Abstract It is crucial for sparse aperture systems to preserve imaging quality, which can be addressed when fast corrections of pistons within a fraction of a wavelength are available. In this paper, we demonstrate that only a single deep convolutional neural network is sufficient to extract pistons from wide-band extended images once being appropriately trained. To eliminate the object characters, the feature vector is calculated as the input by a pair of focused and defocused images. This method possesses the capability of fine phasing with high sensing accuracy, and a large-scale capture range without the use of combined wavelengths. Simple and fast, the proposed technique might find wide applications in phasing telescope arrays or segmented mirrors.

中文翻译：

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通过单个卷积神经网络对具有宽带扩展对象的稀疏孔径系统进行活塞传感

摘要 对于稀疏孔径系统来说，保持成像质量至关重要，当可以在一小部分波长内快速校正活塞时，可以解决这个问题。在本文中，我们证明，一旦经过适当训练，仅单个深度卷积神经网络就足以从宽带扩展图像中提取活塞。为了消除物体特征，特征向量被计算为一对聚焦和散焦图像的输入。该方法具有精细定相和高传感精度的能力，以及不使用组合波长的大规模捕获范围。所提出的技术简单而快速，可能会在定相望远镜阵列或分段反射镜中得到广泛应用。