2D shape reconstruction of irregular particles with deep learning based on interferometric particle imaging

Abstract

Interferometric particle imaging (IPI) technology is widely used in the measurement of various particles. Obtaining particle shape information directly by IPI is challenging because of the complex relationship between the speckle distribution of interference-defocused speckle patterns and the shape of the corresponding irregular particles. Considering this challenge, we implement a deep learning method based on the convolutional neural network (CNN) to reconstruct defocused images of sand particles with sparse features. We also introduce the negative Pearson correlation coefficient as the loss function. To verify the feasibility of our method, we implemented it to reconstruct defocused images obtained from IPI experiments. Finally, compared with another common CNN-based structure, we confirmed that our network structure has good performance in the shape reconstruction of irregular particles.