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. Remedying the drawbacks of conventional iterative methods which are easiliy trapped into local minimum solutions and sensitive to noise, 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.

翻译：傅里叶相位恢复对于跨领域纳米级结构的高清成像至关重要，特别是在相干衍射成像领域。本研究提出了单隐含坐标网络（SCAN），这是一个基于坐标神经网络构建的工具，旨在提升相位恢复性能。针对传统迭代方法易陷入局部最优解且对噪声敏感的缺陷，SCAN以无监督方式将物体坐标与其振幅和相位无缝连接至统一网络中。现有方法多仅使用傅里叶幅度构建损失函数，而我们的方法同时引入预测的幅度和相位，从而提升恢复精度。综合测试验证了SCAN在精度和噪声鲁棒性方面优于传统方法及其他深度学习模型。我们还证明了SCAN在叠层成像场景中表现卓越。