IST and EP implemented a machine learning-enhanced amplitude and phase retrieval process using convolutional neural networks (CNNs), refining diffraction pattern analysis by filtering noise and improving reconstruction quality. A pseudothermal light source benchmark was developed to simulate realistic IDI patterns, providing a flexible testing environment that supports the accuracy and adaptability of the reconstruction models.
Additionally, several techniques, to advance noise filtering and reconstruction techniques, such as rolling convolution algorithms were applied to distinguish coherent signals from noise, achieving cleaner reconstructions that are essential for nanoscale imaging.
IST developed optimized photon counting algorithms compatible with CCDs operating in the single-photon regime, while LUH adapted K photon detection for the Advacam sensor, preparing the IDI setup for MHz-rate data acquisition. These developments align with the anticipated need for high-speed, spectral X-ray imaging, making the IDI platform ready for spectral and correlation imaging applications.
Photon counting algorithms for CMOS high-speed detectors were successfully adapted to MHz repetition rates, preparing the system for spectral optimization and correlation imaging applications. High-Damage-Threshold Coatings were developed by NANEO to meet the high-repetition-rate requirements of the laser. Moreover, Initial machine learning applications, including CNN-based real-time aberration correction, demonstrated effective beam adjustments that stabilized X-ray output.
A simplified single aperture prototype was developed. This was followed by a pixelated SLM tailored to XCAN geometry, designed and manufactured by Arcoptrix. This leads to proceed in time to the nanoplasmonic studies which provided very satisfying and results and we are confident to match better field enhancement by transfering this experimental protocol at the larger XCAN facility in short time.
