Fully automated deep learning-based resolution recovery

Matthew Andrew; Andriy Andreyev; Faguo Yang; Masako Terada; Allen Gu; Robin White

doi:10.1117/12.2633095

10 November 2022 Fully automated deep learning-based resolution recovery

Matthew Andrew, Andriy Andreyev, Faguo Yang, Masako Terada, Allen Gu, Robin White

Proceedings Volume 12242, Developments in X-Ray Tomography XIV; 122420M (2022) https://doi.org/10.1117/12.2633095
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

A novel automated workflow for the recovery of image resolution using deep convolutional neural networks (CNNs) trained using spatially registered multiscale data is presented. Spatial priors, coupled with high order voxel-based image registration, are used to correct for uncertainties in image magnification and position. A network is then trained to remove the effects of point spread from the low-resolution data, improving resolution while reducing image noise and artefact levels. While benchmarking on real materials, including biological, materials science and electronics samples, we find that resolution recovery improves quantitative and qualitative measurements, even if certain image details cannot be easily identified from the original low-resolution data.

Conference Presentation

Citation Download Citation

Matthew Andrew, Andriy Andreyev, Faguo Yang, Masako Terada, Allen Gu, and Robin White "Fully automated deep learning-based resolution recovery", Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122420M (10 November 2022); https://doi.org/10.1117/12.2633095

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