Subspace modeling for fast and high-sensitivity X-ray chemical imaging
This work addresses a bottleneck in nanoscale chemical imaging for scientific and industrial applications, but it is incremental as it improves an existing technique rather than introducing a new paradigm.
The paper tackled the problem of poor signal-to-noise ratios in TXM-XANES imaging, which limits fast and high-sensitivity chemical imaging, by introducing a denoising approach based on subspace modeling, resulting in improved image quality as demonstrated in experiments.
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.