Subspace modeling for fast and high-sensitivity X-ray chemical imaging

TL;DR

This paper introduces a subspace modeling-based denoising method for TXM-XANES imaging, significantly enhancing image quality and enabling faster, more sensitive chemical imaging at the nanoscale.

Contribution

It presents a novel subspace modeling approach that improves TXM-XANES image denoising, facilitating rapid and high-sensitivity chemical imaging.

Findings

Superior denoising performance demonstrated on synthetic data.

Enhanced image quality in real experimental datasets.

Enables faster acquisition with maintained high sensitivity.

Abstract

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.