Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample; therefore, approaches to reduce the scan points are highly desired. However, reconstructions with less number of scan points lead to imaging artifacts and significant distortions, hindering a quantitative evaluation of the results. To address this bottleneck, we propose a generative model combining deep image priors with deep generative priors. The self-training approach optimizes the deep generative neural network to create a solution for a given dataset. We complement our approach with a prior acquired from a previously trained discriminator network to avoid a possible divergence from the desired output caused by the noise in the measurements. We also suggest using the total variation as a complementary before combat artifacts due to measurement noise. We analyze our approach with numerical experiments through different probe overlap percentages and varying noise levels. We also demonstrate improved reconstruction accuracy compared to the state-of-the-art method and discuss the advantages and disadvantages of our approach.

翻译：光学成像技术是一种牢固的、连贯的分解成像技术,能够以纳米尺度对样品进行非侵入性成像,在国防工业或材料科学等不同领域广泛使用。光学学学的一个主要限制是,由于对样品进行机械扫描,数据采集时间很长;因此,非常希望减少扫描点;然而,扫描点较少,重建就会导致成像制品和重大扭曲,从而妨碍对结果进行定量评估。为解决这一瓶颈问题,我们提出了一个基因模型,将深层图像前身与深层基因前科相结合。自我培训方法优化了深层基因神经网络,以便为某一数据集创造解决办法。我们的方法与以前从经过训练的偏差网络获得的一条方法相辅相成,以避免与测量噪音所预期的产出可能存在的差异。我们还建议在测量噪音之前使用总差异作为作战艺术品的补充。我们用数字实验的方法通过不同探测重叠的百分比和不同噪声水平来分析。我们还表明,与现状方法相比,我们的重建准确度有所提高,并讨论了我们所处的劣势和劣势。