Subspace Modeling Enabled High-Sensitivity X-Ray Chemical Imaging
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
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 that 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 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 characterization. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.
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关键词
X-ray,chemical imaging,image restoration
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