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Subspace Modeling for Fast and High-sensitivity X-ray Chemical Imaging


Chen,  Bin
Computer Graphics, MPI for Informatics, Max Planck Society;

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Li, J., Chen, B., Zan, G., Qian, G., Pianetta, P., & Liu, Y. (2022). Subspace Modeling for Fast and High-sensitivity X-ray Chemical Imaging. Retrieved from https://arxiv.org/abs/2201.00259.

Cite as: https://hdl.handle.net/21.11116/0000-000C-C7BE-E
Resolving morphological chemical phase transformations at the nanoscale is of
vital importance to many scientific and industrial applications across various
disciplines. The TXM-XANES imaging technique, by combining full field
transmission X-ray microscopy (TXM) and X-ray absorption near edge structure
(XANES), has been an emerging tool which operates by acquiring a series of
microscopy images with multi-energy X-rays and fitting to obtain the chemical
map. Its capability, however, is limited by the poor signal-to-noise ratios due
to the system errors and low exposure illuminations for fast acquisition. In
this work, by exploiting the intrinsic properties and subspace modeling of the
TXM-XANES imaging data, we introduce a simple and robust denoising approach to
improve the image quality, which enables fast and high-sensitivity chemical
imaging. Extensive experiments on both synthetic and real datasets demonstrate
the superior performance of the proposed method.