X-ray microspectroscopic techniques are essential for studying morphological
and chemical changes in materials, providing high-resolution structural and
spectroscopic information. However, its practical data analysis for reliably
retrieving the chemical states remains a major obstacle to accelerating the
fundamental understanding of materials in many research fields. In this work,
we propose a novel data formulation model for X-ray microspectroscopy and
develop a dedicated unmixing framework to solve this problem, which is robust
to noise and spectral variability. Moreover, this framework is not limited to
the analysis of two-state material chemistry, making it an effective
alternative to conventional and widely-used methods. In addition, an
alternative directional multiplier method with provable convergence is applied
to obtain the solution efficiently. Our framework can accurately identify and
characterize chemical states in complex and heterogeneous samples, even under
challenging conditions such as low signal-to-noise ratios and overlapping
spectral features. Extensive experimental results on simulated and real
datasets demonstrate its effectiveness and reliability.

提出了一种新的 X 射线显微光谱数据表达模型及专用反混合框架，可可靠地检索材料的化学状态，即使在信噪比低和重叠光谱特征等挑战条件下，也能准确识别复杂和异质样品中的化学状态。