Microplastic (MP) pollution has become a critical environmental concern, posing significant threats to ecosystems and human health. Conventional MP detection methods, such as spectrum and thermal analysis, are labor-intensive and time-consuming, hindering effective mitigation efforts.

To combat MP pollution, we delve into the development of novel and efficient detection tools using cutting-edge computational optical imaging techniques. Digital holography enables us to record the wavefront information of objects, offering valuable insights into their morphology, refractive index, and roughness. Additionally, polarization imaging allows us to examine the optical anisotropy of MPs, which is directly linked to their birefringence and material characteristics. By combining these techniques, we can develop an intelligent polarization holographic imaging system that boasts a compact optical configuration, low cost, and high-throughput analysis capabilities. To enhance the accuracy and efficiency of MP characterization and identification, we employ advanced computational methods and leverage the power of deep learning approaches.

What You Will Learn:
• Understand the serious environmental impacts and health risks associated with MP pollution.
• Learn about the development of an intelligent polarization holographic imaging system.
• Explore the application of advanced computational methods and deep learning approaches for accurate MP characterization and identification.

About the Presenter: Edmund Lam from The University of Hong Kong

Edmund Lam is a professor in electrical and electronic engineering at the University of Hong Kong, and directs the Imaging Systems Laboratory. His work spans different aspects of computational optical imaging, from systems and algorithms to applications. He is a Fellow of Optica, SPIE, IEEE, IOP, and IS&T. He was the chair of the Optica Imaging Sensing and Pattern Recognition Technical Group from 2017-19.