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	Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-To-noise ratio (SNR) SEM images: analysis with SMILE Sacchi, S., Dey, B., Mochi, I., Halder, S., & Leray, P. (2023). Deep learning denoiser assisted roughness measurements extraction from thin resists with low signal-To-noise ratio (SNR) SEM images: analysis with SMILE. In P. P. Naulleau, P. A. Gargini, T. Itani, & K. G. Ronse (Eds.), Proceedings of SPIE - the international society for optical engineering: Vol. 12750. International conference on extreme ultraviolet lithography (p. 1275010 (12 pp.). https://doi.org/10.1117/12.2687639 Detailed Record Published Version
	Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography images Birjukovs, M., Trtik, P., Kaestner, A., Hovind, J., Klevs, M., Gawryluk, D. J., … Jakovics, A. (2021). Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography images. Applied Sciences, 11(20), 9710 (40 pp.). https://doi.org/10.3390/app11209710 Detailed Record Published Version
	Processing neutron imaging data - quo vadis? Kaestner, A. P., & Schulz, M. (2015). Processing neutron imaging data - quo vadis? In E. H. Lehmann, A. P. Kaestner, & D. Mannes (Eds.), Physics procedia: Vol. 69. Proceedings of the 10th world conference on neutron radiography (WCNR-10) Grindelwald, Switzerland October 5-10, 2014 (pp. 336-342). https://doi.org/10.1016/j.phpro.2015.07.047 Detailed Record Published Version