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Follow on Google News | Diffractive networks enable quantitative phase imaging (QPI) through random diffusersBy: UCLA ITA In a recent paper published in Light: Advanced Manufacturing, a research team led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA) reported a new methodology for quantitative phase imaging of objects that are completely covered by random, unknown phase diffusers. Their method uses a diffractive optical network composed of successive transmissive layers optimized via deep learning, and this diffractive system axially spans ~70λ, where λ is the illumination wavelength. During its training, various randomly generated phase diffusers were utilized to build resilience against phase perturbations created by random unknown diffusers. After the training, which is a one-time effort, the resulting diffractive layers can perform all-optical phase recovery and quantitative phase imaging of unknown objects that are entirely hidden by unknown random diffusers. In their numerical simulations, the team successfully demonstrated the capability of the QPI diffractive network to achieve imaging of new objects through new random phase diffusers that were never seen before. In addition, their research delved into the impact of various factors, such as the number of spatially-structured diffractive layers and the trade-off between image quality and output energy efficiency, revealing that deeper diffractive optical networks could generally outperform shallower designs. This QPI diffractive network can be physically scaled to operate at different parts of the electromagnetic spectrum without redesigning or retraining its layers. Such an all-optical computing framework possesses the benefits of low power consumption, high frame rate, and compact size. The UCLA research team anticipates the potential integration of their QPI diffractive designs onto image sensor chips (CMOS/CCD imagers), effectively transforming a standard optical microscope into a diffractive QPI microscope capable of performing on-chip phase recovery and image reconstruction through light diffraction within passive structured layers. See the article: https://doi.org/ End
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