ausblenden:
Schlagwörter:
eess.IV,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Multimedia, cs.MM
Zusammenfassung:
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.
