Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network

TL;DR

This paper presents a CNN-based method using a UNET architecture with residual blocks to accurately reconstruct the 3D shapes of irregular particles from interferometric images, validated on experimental data.

Contribution

It introduces a novel CNN approach trained on a large experimental dataset for 3D shape reconstruction of irregular particles from interferometric images.

Findings

High accuracy in shape reconstruction of various particle types

Effective 3D reconstruction from three different viewpoints

Demonstrated on experimentally generated interferometric images

Abstract

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.