论文标题
深度学习破译增强的第二个谐波成像
Second Harmonic Imaging Enhanced by Deep Learning Decipher
论文作者
论文摘要
波前传感和重建被广泛用于自适应光学元件，畸变校正和高分辨率光相成像。传统上，干扰和/或微芯阵列用于将光相转换为强度变化。扭曲波前的直接成像通常会导致复杂的相位检索，并且对比度低，灵敏度低。在这里，基于编码为第二个谐波信号的相位敏感信息开发并实验证明了一种新颖的方法，该信息对波前调制本质上敏感。通过设计和实施深度神经网络，我们演示了深度学习破译（SHIELD）增强的第二种谐波成像，以提高效率和弹性的相位检索。 Shield遗传了两光子显微镜的优势，表现出比λ/100更好的敏感性，无参考和视频速率相成像，并且对噪声的鲁棒性更好，从而促进了从生物成像到波前传感的许多应用。
Wavefront sensing and reconstruction are widely used for adaptive optics, aberration correction, and high-resolution optical phase imaging. Traditionally, interference and/or microlens arrays are used to convert the optical phase into intensity variation. Direct imaging of distorted wavefront usually results in complicated phase retrieval with low contrast and low sensitivity. Here, a novel approach has been developed and experimentally demonstrated based on the phase-sensitive information encoded into second harmonic signals, which are intrinsically sensitive to wavefront modulations. By designing and implementing a deep neural network, we demonstrate the second harmonic imaging enhanced by deep learning decipher (SHIELD) for efficient and resilient phase retrieval. Inheriting the advantages of two-photon microscopy, SHIELD demonstrates single-shot, reference-free, and video-rate phase imaging with sensitivity better than λ/100 and high robustness against noises, facilitating numerous applications from biological imaging to wavefront sensing.
