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Convolutional neural networks (CNNs) and digital holographic interferometry (DHI) can be combined to improve the calculation efficiency and to simplify the procedures of many DHI applications. In DHI, for the measurements of concentration differences between liquid samples, two or more holograms are compared to find the difference phases among them, and then to estimate the concentration values. However, liquid samples with high concentration difference values are difficult to calculate using common phase unwrapping methods as they have high spatial frequencies. In this research, a new method to skip the phase unwrapping process in DHI, based on CNNs, is proposed. For this, images acquired by Guerrero-Mendez et al. (Metrology and Measurement Systems 24, 19–26, 2017) were used to train the CNN, and a multiple linear regression algorithm was fitted to estimate the concentration values for liquid samples. In addition, new images were recorded to evaluate the performance of the proposed method. The proposed method reached an accuracy of 0.0731%, and a precision of ±0.0645. The data demonstrated a high repeatability of 0.9986, with an operational range from 0.25 gL−1 to 1.5 gL−1. The proposed method was performed with liquid samples in a cylindrical glass.

Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples