Depth from Defocus Technique For High Number Density Particle Images

Abstract

The Depth from Defocus (DFD) imaging technique is used to measure the size and number concentration of particles in dispersed two-phase flows, but until now it has primarily been applied to low concentration particle images. This study explores how the technique can be extended to handle overlapping images caused by neighboring particles, significantly broadening the application scope of the DFD technique and enabling measurements at higher particle number/volume concentrations. The processing algorithms are experimentally validated using a dedicated apparatus that can systematically vary particle size, shape, and degree of image overlap. Additionally, this study explores the use of Convolutional Neural Networks (CNN) for this task, comparing these results with those obtained using conventional analyses in terms of accuracy, tolerable concentration limits, and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. An image generation procedure for out-of-focus neighboring spherical particles, resulting in a known blurred image overlap, is therefore first developed. This procedure is validated using laboratory images with known particle size distribution, position, and image overlap before creating the teaching dataset. The trained processing scheme is then applied to both synthetic datasets and experimental data, allowing the evaluation of the technique’s limits in terms of image overlap and tolerable volume concentration, as a function of particle size distribution.