B-U-net: Holographic image segmentation of multi-scale dense particle field with noisy training dataset

Abstract

Particle field characterization in reconstructed holographic images especially depicting fuel atomization in aerospace engines has been a challenge due to the multi-scale dense particles with complex structures. For the lack of standard dataset, the difficulties of extracting correct information from wrong labels created by traditional segmentation methods restrict the utilization of supervised deep learning models. This study proposes a deep neural network called B-U-net which contains Bayesian convolution layers to learn the distribution of parameters instead of the fixed values to restrain the overfitting of the noisy labels. The performance of the B-U-net is quantitatively tested in inline and off-axis reconstructed holographic images compared with other three methods. Two metrics named Positive Intersection over Union (PIoU) and adjusted Structural Similarity (SSIM) are applied for evaluation. The highest metric results of B-U-net prove the strong and robust ability in overall predicting and structural accuracy for two different kinds of images.