Fourier ptychography based on multi-scale feature fusion network

SONG Dong-han; WANG Bin; ZHU You-qiang; LIU Xin

doi:10.37188/CJLCD.2022-0094

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Fourier ptychography based on multi-scale feature fusion network

Image Processing | 更新时间：2022-10-28

- Fourier ptychography based on multi-scale feature fusion network
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 11, Pages: 1476-1487(2022)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
  2.中国科学院大学，北京 100049
  3.拉彭兰塔-拉赫蒂理工大学工程科学学院计算机视觉与模式识别实验室，芬兰拉赫蒂15210
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2020YFA0714102)
- DOI：10.37188/CJLCD.2022-0094
  CLC：
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SONG Dong-han, WANG Bin, ZHU You-qiang, et al. Fourier ptychography based on multi-scale feature fusion network. [J]. Chinese Journal of Liquid Crystals and Displays 37(11):1476-1487(2022)
DOI：

SONG Dong-han, WANG Bin, ZHU You-qiang, et al. Fourier ptychography based on multi-scale feature fusion network. [J]. Chinese Journal of Liquid Crystals and Displays 37(11):1476-1487(2022) DOI： 10.37188/CJLCD.2022-0094.

摘要

傅里叶叠层成像是一种实现光学系统高分辨率、大视场成像的技术。传统FP方法的高分辨率重建过程需要较高的孔径重叠率，导致采集图像数量较多，采样效率低。此外，FP重建算法的复杂度高，重建时间长。针对以上问题，本文结合深度学习，提出一种基于多尺度特征融合网络的傅里叶叠层成像算法，通过改进的特征金字塔卷积神经网络，能够从稀疏采样的低分辨振幅图像中提取特征信息并进行融合，实现超分辨的复图像重建。实验结果表明，在相同采样条件下，与传统方法相比，本文提出的深度学习算法提高了图像重建的质量，减少了90%以上的重建时间，并且对高斯噪声的鲁棒性较高。所提出的方法能够将相邻频域子孔径间的重叠率从50%降低至25%，减少50%的采集图像数量，大幅提高采样效率。

Abstract

Fourier Ptychography （FP） is a technology of achieving high-resolution， large field-of-view imaging of optical system. However， the high-resolution reconstruction based on traditional FP methods requires a high aperture overlap ratio， resulting in a large number of captured images and low sampling efficiency. In addition， the FP reconstruction algorithm has high complexity and long reconstruction time. Aiming at solving these problems of the FP， this paper proposes a deep learning algorithm based on multi-scale feature fusion network. Through the improved feature pyramid module， the feature information can be extracted from multiple low-resolution images captured by the FP imaging system， and the information is fused to achieve super-resolution reconstruction. Experimental results show that compared with traditional methods， the deep learning algorithm proposed in this paper improves the quality of image reconstruction， reduces the reconstruction time by 90%， and is more robust to Gaussian noise. In addition， the proposed method can reduce the overlap ratio between sub-apertures from 50% to 25% in frequency domain， and reduce the number of captured images by 50%， greatly improving the sampling efficiency.

关键词

计算成像傅里叶叠层成像特征金字塔稠密连接通道注意力

Keywords

computational imagingfourier ptychographyfeature pyramiddense connectivitychannel attention

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