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Artificial Intelligence applied to X-ray / synchrotron techniques


Artificial intelligence is an emerging tool that is becoming ubiquitous in X-ray and synchrotron facilities. Mindful of its importance and as part of the upgrade of French CRG beamlines at ESRF, we also wish to discuss how these tools can be useful for us and for the community of scientists working on X-rays or synchrotron techniques. Therefore, we will start a series of webinars (online seminars) dedicated to applications of artificial intelligence (machine and deep learning) to techniques based on x-rays and synchrotrons. The next webinar of this series will be:


Artificial intelligence: a tool for high brilliance sources and X-ray imaging

Pablo Villanueva & Yuhe Zhang (Lund University, Sweden)

March 11 at 2:00 pm / 14:00 (CET)

The advent of high brilliance X-ray sources, such as diffraction-limited storage rings and X-ray free-electron lasers, has opened new possibilities for the X-ray imaging community. However, these technologies post new scientific and technical challenges that must be addressed to exploit their capabilities efficiently. One obvious challenge, as a result of the increase in coherent flux or brilliance, is the generation of large amounts of data in a shorter time. This problem results in the necessity to develop real-time trigger and online data analysis algorithms to cope with the data generation. For the case of X-ray imaging and precisely coherent imaging techniques, this problem translates into the urge to develop real-time phase-retrieval algorithms, which can retrieve the missing-phase information when acquiring the data. Nowadays, the most popular solutions to the phase problem are iterative approaches. Iterative approaches are overall time- consuming, requiring at least several seconds to converge for a single image. Thus, they are not suitable for performing online reconstructions. Recently, deep-learning-based methods have been explored that process the potential to perform fast image reconstructions.

In this webinar, we will discuss some of the open challenges at high-brilliant sources, which can be potentially addressed with artificial intelligence approaches. Then, we will focus on the phase problem and how it can be addressed with deep-learning approaches. For such purpose, we will discuss state-of-the-art deep-learning phase-retrieval methods and introduce a new deep-learning approach: PhaseGAN. PhaseGAN is an unpaired approach that enhances the phase-retrieval capabilities of analogous approaches by including the physic of image formation.

The link to connect to the webinar will be sent by e-mail. To receive it, please register for free by clicking here:



For more information about upcoming webinars on this series, please click here.