UCLA

The rapid development of machine learning has transformed conventional optical imaging processes, setting new benchmarks in computational imaging tasks. In this dissertation, we delve into the transformative impact of recent advancements in machine learning on optical imaging processes, focusing on how these technologies revolutionize computational imaging tasks. Specifically, this dissertation centers on two major topics: deep learning-enabled computational microscopy and the all-optical diffractive networks. Optical microscopy has long served as the benchmark technique for diagnosing various diseases over centuries. However, its reliance on high-end optical components and accessories, necessary to adapt to various imaging samples and conditions, often limits its applicability and throughput. Recent advancements in computational imaging techniques utilizing deep learning methods have transformed conventional microscopic imaging modalities, delivering both enhanced speed and superior image quality without introducing extra complexity of the optical systems. In the first topic of this dissertation, we demonstrate that deep learning-enabled image translation approach can significantly benefit a wide range of applications for microscopic imaging. We start with introducing a customized system for single-shot quantitative polarization imaging, capable of reconstructing comprehensive birefringent maps from a single image capture, which offers enhanced sensitivity and specificity in diagnosing crystal-induced diseases. Utilizing these quantitative birefringent maps as a baseline, we employ deep learning tools to convert phase-recovered holograms into quantitative birefringence maps, thereby improving the throughput of crystal detection with simplified system complexity. Extending this concept of deep learning-enabled image translation, we also explore its applications in histopathology. Our technique, termed as “virtual histological staining”, transforms unstained biological samples into visually rich, stained-like images without the need for chemical agents. This innovation minimizes costs, labor, and diagnostic delays as well as opens up new possibilities in histopathology workflow. The evolution of deep learning tools not only facilities the optical image analysis and processing, but also provides guidance in design and enhancement of optical systems. The second topic of this dissertation is the development and application of diffractive deep neural networks (D2NN). Developed with deep learning, D2NNs execute given computational tasks by manipulating light diffraction through a series of engineered surfaces, which is completed at the speed of light propagation with negligible power consumption. Based on this framework, a lot of novel computational tasks can be executed in an all-optical way, which is beyond the capabilities of the traditional optics design approaches. We introduce several all-optical computational imaging applications based on D2NN, including class-specific imaging, class-specific image encryption, and unidirectional image magnification and demagnification, demonstrating the versatility of this promising framework.