Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data

Scientific Reports, vol. 11, pp. 11895

Abstract

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.

Authors 9

1. Allard A. Hendriksen corresponding

   Centrum Wiskunde & Informatica · University of Amsterdam

   Affiliation as printed

   Centrum Wiskunde and Informatica, Amsterdam, The Netherlands. allard.hendriksen@cwi.nl

   Centrum Wiskunde & Informatica and Universiteit of Amsterdam

2. Minna Bührer

   Paul Scherrer Institute · Swiss Light Source

   Affiliation as printed

   Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland

   Paul Scherrer Institute

3. Laura Leone

   University of Milan

   Affiliation as printed

   Dipartimento di Scienze della Terra, Università degli Studi di Milano, Milan, Italy

   Università degli Studi di Milano = University of Milan

4. Marco Merlini

   University of Milan

   Affiliation as printed

   Dipartimento di Scienze della Terra, Università degli Studi di Milano, Milan, Italy

   Università degli Studi di Milano = University of Milan

5. Nicola Viganò

   European Synchrotron Radiation Facility

   Affiliation as printed

   ESRF - The European Synchrotron, Grenoble, France

   European Synchroton Radiation Facility [Grenoble]

6. Daniël M. Pelt Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica · University of Amsterdam

   Affiliation as printed

   Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Leiden Universiteit, Leiden, The Netherlands

   Centrum Wiskunde & Informatica and Universiteit of Amsterdam

   Universiteit Leiden = Leiden University

7. Federica Marone

   Paul Scherrer Institute · Swiss Light Source

   Affiliation as printed

   Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland

   Paul Scherrer Institute

8. Marco Di Michiel

   European Synchrotron Radiation Facility

   Affiliation as printed

   ESRF - The European Synchrotron, Grenoble, France

   European Synchroton Radiation Facility [Grenoble]

9. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica · University of Amsterdam

   Affiliation as printed

   Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Leiden Universiteit, Leiden, The Netherlands

   Centrum Wiskunde & Informatica and Universiteit of Amsterdam

   Universiteit Leiden = Leiden University