vyastreb / sem2surface

SEM/BSE 3D Surface Reconstruction

Overview

This repository contains a Python-based solution for 3D surface reconstruction from SEM/BSE images captured using a minimum of three detectors. The methodology leverages the Principal Component Analysis (PCA) of the captured images to discern the principal component images [1]. To reorient gradient images along $x$ and $y$ axes, the Radon transform is used. The final 3D surface, represented as (z(x,y)), is derived from its gradients either through the Frankot and Chellappa method [2] or via direct integration paired with a minimization process between adjacent profiles.

Features

• Intuitive GUI: A user-friendly simplistic interface built with Python's Tkinter allows for easy image uploads and 3D surface construction.
• Comprehensive Outputs: Each execution yields:
  • 3 original images and their PCA decomposition (PNG format).
  • Radon transform RMS change wrt the rotation angle (PDF format).
  • Gradients visualisation along $x$ and $y$ (PNG format).
  • 3D map of the reconstructed surface (2 PNG files).
  • ASCII representation of the reconstructed surface with (x,y,z) columns.
  • Surface roughness data (NPZ format).
  • Detailed log file capturing all operations (TXT format).

Getting Started

To launch the interface, execute the following command:

$ python sem2surface.py

Users can easily upload a minimum of three images (supported formats: JPG, PNG, TIFF, BMP) and initiate the reconstruction process by clicking the "3D Reconstruct" button.

Repository Structure

• src/
  • sem2surface.py: Core module for 3D surface reconstruction from SEM/BSE images.
  • sem2surface_gui.py: GUI module.
  • logo.png, logo.svg: Application logo.
• doc/
  • SEM2surface.pdf: Concise documentation.
• example/
  • Sample SEM images from different detectors.
  • Sample output of the reconstructed surface.
• README.md: This file.

References

• [1] Neggers, J., Héripré, E., Bonnet, M., Boivin, D., Tanguy, A., Hallais, S., Gaslain, F., Rouesne, E. and Roux, S. (2021). Principal image decomposition for multi-detector backscatter electron topography reconstruction. Ultramicroscopy, 227:113200. DOI
• [2] Frankot, R. T., & Chellappa, R. (1988). A method for enforcing integrability in shape from shading algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4):439-451. DOI

Additional Information

• Developer: Vladislav A. Yastrebov
  • Affiliation: CNRS, MINES Paris, PSL, Centre des matériaux
  • Date: August 2023
  • yastrebov.fr
• Licence: BSD 3-Clause License.

Acknowledgements

The code was developed with the assistance of GPT-4, CoderPad plugin, and Copilot in VSCode.