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基于稀疏采样的远距离亚衍射超分辨率成像。

Long-Distance Sub-Diffraction High-Resolution Imaging Using Sparse Sampling.

机构信息

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Sensors (Basel). 2020 May 31;20(11):3116. doi: 10.3390/s20113116.

Abstract

How to perform imaging beyond the diffraction limit has always been an essential subject for the research of optical systems. One effective way to achieve this purpose is Fourier ptychography, which has been widely used in microscopic imaging. However, microscopic imaging measurement technology cannot be directly extended to imaging macro objects at long distances. In this paper, a reconstruction algorithm is proposed to solve the need for oversampling low-resolution images, and it is successfully applied to macroscopic imaging. Compared with the traditional FP technology, the proposed sub-sampling method can significantly reduce the number of iterations in reconstruction. Experiments prove that the proposed method can reconstruct low-resolution images captured by the camera and achieve high-resolution imaging of long-range macroscopic objects.

摘要

如何实现超越衍射极限的成像一直是光学系统研究的一个重要课题。实现这一目的的一种有效方法是傅里叶叠层术,它已被广泛应用于微观成像。然而,微观成像测量技术不能直接扩展到远距离的宏观物体成像。本文提出了一种重建算法,以解决对低分辨率图像进行过采样的需求,并成功地应用于宏观成像。与传统的 FP 技术相比,所提出的子采样方法可以显著减少重建过程中的迭代次数。实验证明,该方法可以重建相机拍摄的低分辨率图像,并实现远距离宏观物体的高分辨率成像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae2c/7309043/5d0abad04b7e/sensors-20-03116-g001.jpg

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