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

arXiv: https://arxiv.org/abs/2201.00259 · HTML: https://mlsanity.com/paper/2201.00259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications](https://mlsanity.com/paper/1605.01710/) (2016)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [Plug and play methods for magnetic resonance imaging (long version)](https://mlsanity.com/paper/1903.08616/) (2019)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [An Online Plug-and-Play Algorithm for Regularized Image Reconstruction](https://mlsanity.com/paper/1809.04693/) (2018)
- [Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning](https://mlsanity.com/paper/1904.02816/) (2019)
- [Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium](https://mlsanity.com/paper/1705.08983/) (2017)
- [Unsupervised Deep Video Denoising](https://mlsanity.com/paper/2011.15045/) (2020)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence](https://mlsanity.com/paper/2009.00071/) (2020)
- [TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks](https://mlsanity.com/paper/1902.07582/) (2019)
- [SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors](https://mlsanity.com/paper/1911.13241/) (2019)
- [Restore from Restored: Video Restoration with Pseudo Clean Video](https://mlsanity.com/paper/2003.04279/) (2020)

## Abstract (arXiv)

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.