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Self-adapting denoising, alignment and reconstruction in electron tomography in materials science
Tony Printemps1, Guido Mula2, Daniele Sette1
1Université Grenoble Alpes, F-38000 Grenoble, France; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France.
Ultramicroscopy
|September 29, 2015
Summary
This study introduces an automated electron tomography procedure for needle-shaped samples, enhancing 3D reconstruction speed and accuracy. The method significantly reduces noise and artifacts, enabling faster, more reliable analysis of materials like porous silicon and silver nanoparticles.
Area of Science:
- Materials Science
- Nanotechnology
- Microscopy
Background:
- Electron tomography (ET) is a powerful technique for 3D imaging.
- Manual processing of ET data is time-consuming and prone to errors.
- Needle-shaped samples are suitable for ET but require specialized processing.
Purpose of the Study:
- To develop an automated procedure for electron tomography.
- To improve the speed, accuracy, and robustness of 3D reconstruction in ET.
- To reduce artifacts and noise in ET data for enhanced analysis.
Main Methods:
- Self-adapting denoising using undecimated wavelet transforms for mixed Poisson-Gaussian noise.
- Automatic alignment combining cross-correlations, common line algorithm, and intermediate reconstructions.
- Accurate tilt axis detection and correction.
Main Results:
- Robust and automatic noise parameter estimation and denoising.
- Efficient and fast alignment by mixing multiple techniques.
- Significant improvements in simulations and real data (porous silicon, silver nanoparticles).
- Reduced artifacts and noise in 3D reconstructions with minimal user intervention.
Conclusions:
- The automated ET procedure is effective for needle-shaped inorganic samples.
- The method enables faster (under an hour) and more reliable 3D reconstructions.
- This advancement facilitates routine use of electron tomography for quantitative analysis.
