Abstract
Characterization of particle size and shape is important in studying particle field changes. Interferometric particle imaging is a reliable technique that is widely used in the characterization of droplets or bubbles. In recent years, this method has also been used to measure irregular rough particles. According to the distribution of emitting points, the projected 2D shape of particles can be obtained. In this work, we propose a convolutional neural network (CNN) to obtain the projection image of an irregular particle from its interference-defocused image through the relationship between the distributions of particle emitting points and speckles in the defocused image. Sand projections could be successfully predicted from the experimental defocused image by the trained CNN, and difference in caliper lengths between the actual and predicted particle projections was within 6%. Such a small variation proves the feasibility and accuracy of the proposed method.
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