Holographic Classifier: Deep Learning in Digital Holography for Automatic Micro-objects Classification

Abstract

Micro-objects, such as microplastics and particulate pollution, need to be accurately observed and detected by high-precision optical systems. Digital holography is a powerful tool to detect such microscopic objects. However, traditional digital holography requires additional image processing such as phase unwrapping, de-noising, and refocusing, which costs a lot of time and does not have a consistently better performance in micro-object detection. Here, we propose an intelligent holographic classifier, which is a deep learning-based lensless inline digital holography system to detect the micro-object directly on the raw holograms and show the quantitative information of micro-objects for individual hologram by automatic object classification. In a demonstration where we capture the holograms of microplastics particles, which are easily confused with dust particles, we arrive at an accuracy above 97%. Compared with other leading classifiers, our method has shorter training time, faster classification and quantitative analysis, higher accuracy, and better robustness. Furthermore, this intelligent digital holography system, which requires only a light-emitting diode (LED), a sample slide, and a CMOS camera, can be used as a portable low-cost microplastics counting and classification tool, driving the development of microplastics detection in the ecological environment.