![]() Optical metrology is a broad and interdisciplinary field relating to diverse disciplines such as photomechanics, optical imaging, and computer vision. As a result, optical metrology is being increasingly adopted in many applications where reliable data about the distance, displacement, dimensions, shape, roughness, surface properties, strain, and stress state of the object under test are required 8, 9, 10. The speed of light defines the international standard of length, the meter, as the length traveled in vacuum during a time interval of 1/299,792,458 of a second 7. For example, optical interferometry takes advantage of the wavelength of light as a precise dividing marker of length. In optical metrology, these fundamental properties of light are ingeniously utilized as information carriers of a measurand, enabling a wide range of optical metrology tools that allow the measurement of a wide range of subjects 4, 5, 6. Light is characterized by its fundamental properties, namely, amplitude, phase, wavelength, direction, frequency, speed, polarization, and coherence. Optical metrology is the science and technology of making measurements with the use of light as standards or information carriers 1, 2, 3. Finally, the directions for future research are outlined. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. ![]() Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. ![]() ![]() In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. ![]()
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