Fourier phase retrieval is essential for high-definition imaging of nanoscale
structures across diverse fields, notably coherent diffraction imaging. This
study presents the Single impliCit neurAl Network (SCAN), a tool built upon
coordinate neural networks meticulously designed for enhanced phase retrieval
performance. Bypassing the pitfalls of conventional iterative methods, which
frequently face high computational loads and are prone to noise interference,
SCAN adeptly connects object coordinates to their amplitude and phase within a
unified network in an unsupervised manner. While many existing methods
primarily use Fourier magnitude in their loss function, our approach
incorporates both the predicted magnitude and phase, enhancing retrieval
accuracy. Comprehensive tests validate SCAN's superiority over traditional and
other deep learning models regarding accuracy and noise robustness. We also
demonstrate that SCAN excels in the ptychography setting.

研究介绍了一种名为 Single impliCit neurAl Network (SCAN) 的工具，它是基于精心设计的坐标神经网络，用于提升相位恢复性能和精度，在高清晰度成像领域和相干衍射成像中具有重要应用价值。SCAN 通过无监督的方式将目标坐标与其振幅和相位相连接，绕过了常规迭代方法的局限性，如高计算负荷和噪声干扰，并将预测振幅和相位都纳入损失函数，提高了恢复的准确性。综合测试结果证实了 SCAN 相对于传统方法和其他深度学习模型在准确性和噪声鲁棒性方面的优越性，同时还展示了 SCAN 在斑图成像设置中的出色表现。