Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network
<|reference_start|>Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network: We have developed a convolutional neural network (CNN) to reconstruct the shape of irregular rough particles from their interferometric images. The CNN is based on a UNET architecture with residual block modules. The database has been constructed using the experimental patterns generated by perfectly known pseudo-particles programmed on a Digital Micromirror Device (DMD) and under laser illumination. The CNN has been trained on a basis of 18000 experimental interferometric images using the AUSTRAL super computer (at CRIANN in Normandy). The CNN is tested in the case of centrosymmetric (stick, cross, dendrite) and non-centrosymmetric (like T, Y or L) particles. The size and the 3D orientation of the programmed particles are random. The different shapes are reconstructed by the CNN with good accuracy. Using three angles of view, the 3D reconstruction of particles from three reconstructed faces can be further done.<|reference_end|>
@article{abad2024reconstruction,
    title={Reconstruction of the shape of irregular rough particles from their
  interferometric images using a convolutional neural network},
    author={Alexis Abad, Alexandre Poux (CORIA), Alexis Boulet, Marc Brunel
  (CORIA)},
    journal={21st International Symposium on the Application of Laser and
  Imaging Techniques to Fluid Mechanics?LISBON | PORTUGAL ?JULY 8 -- 11, 2024,
  Jul 2024, Lisbonne, Portugal},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03327},
    primaryClass={cs.CV cs.AI}
}