Yamada Koki, Akaishi Natsuki, Yatabe Kohei, Takayama Yuki
Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho, Koganei Tokyo Japan.
International Center for Synchrotron Radiation Innovation Smart, Tohoku University, 468-1 Aoba-ku, Sendai, Japan.
J Appl Crystallogr. 2024 Aug 19;57(Pt 5):1323-1335. doi: 10.1107/S1600576724006897. eCollection 2024 Oct 1.
Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.
叠层成像术是一种强大的计算成像技术，具有显微成像能力且能适应各种样本。为了获得成像结果，它需要一种相位恢复算法，其性能直接决定成像质量。最近，已提出基于深度神经网络（DNN）的相位恢复方法，以从普通的基于模型的迭代算法提高成像质量。然而，基于DNN的方法存在一些局限性，因为它们对实验条件的变化敏感，并且难以收集足够的测量样本图像来训练DNN。为了克服这些局限性，提出了一种结合基于模型和基于DNN方法的叠层成像相位恢复算法。该方法利用基于DNN的去噪器来辅助像ePIE这样的迭代算法找到更好的重建图像。DNN和迭代算法的这种结合允许将测量模型明确纳入基于DNN的方法中，提高其对实验条件变化的鲁棒性。此外，为了规避收集训练数据的困难，建议使用公式驱动的监督方法系统地生成合成图像来训练基于DNN的去噪器，而不使用实际测量的样本图像。在基于硬X射线叠层成像测量系统的模拟实验中，通过将该方法与ePIE和rPIE进行比较，评估了所提出方法的成像能力。这些结果表明，即使对于低光照强度的数据，所提出的方法也能够以ePIE和rPIE所需迭代次数的一半重建更高空间分辨率的图像。此外，所提出的方法对其超参数具有鲁棒性。此外，所提出的方法应用于在SPring-8 BL24XU测量的西门子星图和墨粉颗粒的叠层成像数据集，这证实了它能够从照明区域重叠率低于ePIE和rPIE要求的测量扫描中成功重建图像。
