Ptychography is a diffraction-based x-ray microscopy method that removes the resolution limit imposed by image-forming optical elements. However, background noise in the recorded diffraction patterns will degrade the reconstructed images and may cause reconstruction failure. Removal of the background noise from a ptychography dataset is an important but rather ambiguous prereconstruction data processing step because high-spatial-frequency diffraction signals are inevitably partly wiped out along with the noise. In this paper, several newly designed techniques for removing background noise from experimental ptychographic datasets are provided. Meanwhile, effects of residual background noise and high-frequency signal loss on reconstructed image quality are discussed in detail. The image quality is assessed quantitatively by the power spectral density analysis method and spatial resolution calculation. Both the simulated and experimental results indicate that the positive effect of noise removal by these methods clearly exceeds the negative effect of the accompanied high-spatial-frequency signal loss because part of the lost signals can be recovered by the improved consistencies between neighboring diffraction patterns by the noise removal.

Coherent diffractive imaging (CDI) is a lensless, high-resolution imaging method that is currently under rapid development by using X-rays, visible light, or electrons. However, its inherent ambiguities and the need of a priori knowledge about objects are limiting applications based on this method. By combining a conventional CDI method with the basic idea of ptychography, the method of spatially correlated CDI, a method fit for multiple sample imaging in single-shot mode, is proposed to enhance the robustness of CDI. In this method, a strong spatial relevancy is built by introducing the same region into each sample, which establishes a strong real-space constraint for image reconstruction. Both simulations and visible-light experiments demonstrated the good imaging quality and robustness of this method, which does not need tight supports.

With recent technological advances, large-scale experimental facilities generate huge datasets, into the petabyte range, every year, thereby creating the Big Data deluge effect. Data management, including the collection, management, and curation of these large datasets, is a significantly intensive precursor step in relation to the data analysis that underpins scientific investigations. The rise of artificial intelligence (AI), machine learning (ML), and robotic automation has changed the landscape for experimental facilities, producing a paradigm shift in how different datasets are leveraged for improved intelligence, operation, and data analysis. Therefore, such facilities, known as superfacilities, which fully enable user science while addressing the challenges of the Big Data deluge, are critical for the scientific community. In this work, we discuss the process of setting up the Big Data Science Center within the Shanghai Synchrotron Radiation Facility (SSRF), China’s first superfacility. We provide details of our initiatives for enabling user science at SSRF, with particular consideration given to recent developments in AI, ML, and robotic automation.