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Untrained physics-driven aberration retrieval network
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DOI：10.1364/OL.523377.png)
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摘要
En 中文
In the field of coherent diffraction imaging, phase retrieval is essential for correcting the aberration of an optic system. For estimating aberration from intensity, conventional methods rely on neural networks whose performance is limited by training datasets. In this Letter, we propose an untrained physics-driven aberration retrieval network (uPD-ARNet). It only uses one intensity image and iterates in a self-supervised way. This model consists of two parts: an untrained neural network and a forward physical model for the diffraction of the light field. This physical model can adjust the output of the untrained neural network, which can characterize the inverse process from the intensity to the aberration. The experiments support that our method is superior to other conventional methods for aberration retrieval. (c) 2024 Optica Publishing Group
Keyword:
DIGITAL HOLOGRAPHIC MICROSCOPY
PHASE-RETRIEVAL
NEURAL-NETWORKS
COMPENSATION
DIVERSITY
MAGNIFICATION
期刊
IF:
3.3
论文数:
4.0W
被引数:
7.6W
机构
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