光学学报, 2020, 40 (20): 2011001, 网络出版: 2020-09-19
基于三维卷积神经网络的彩色傅里叶叠层显微术 下载: 1226次
Color Fourier Ptychography Microscopy Using Three-Dimensional Convolutional Neural Network
成像系统 显微术 傅里叶叠层显微成像 卷积神经网络 主成分分析 彩色图像重建 imaging systems microscopy Fourier ptychography microscopy convolutional neural network principal component analysis colored-image reconstruction
摘要
包含多波长信息的低分辨(LR)灰度图难以被完全解复用,根据LR图像信息重建出的彩色高分辨(HR)图像容易出现通道串扰的现象。为重建不受通道串扰干扰的彩色HR图像,提出一种基于三维卷积神经网络(CNN)的彩色HR图像重建算法。采用主成分分析法提取单色HR图像和彩色LR图像的结构信息,然后基于结构信息训练CNN来建立单色HR图像和彩色LR图像之间的映射关系,最后生成彩色HR图像。实验结果表明,所提算法可以获得不受通道串扰影响、色彩不失真的彩色HR图像。定量评价指标方均根误差小于0.1,结构相似性参数大于0.9。
Abstract
Low-resolution (LR) grayscale images with multi-wavelength information are difficult to fully demultiplex. High-resolution (HR) colored images reconstructed from LR images are prone to channel crosstalk. To reconstruct HR colored images that are not prone to channel crosstalk, we propose an HR colored image reconstruction algorithm based on a three-dimensional convolutional neural network (CNN). The principal component analysis method is used to extract structural information from HR monochromatic images and LR colored images, and then the CNN is trained based on the structural information to establish a mapping relationship between the HR monochromatic image and LR colored image. Consequently, a HR colored image is generated. The experimental results show that the proposed algorithm can obtain HR colored images without channel crosstalk and color distortion. The quantitative evaluation indexs show that the root mean square error and structural similarity parameter are less than 0.1 and greater than 0.9, respectively.
张慕阳, 梁艳梅. 基于三维卷积神经网络的彩色傅里叶叠层显微术[J]. 光学学报, 2020, 40(20): 2011001. Muyang Zhang, Yanmei Liang. Color Fourier Ptychography Microscopy Using Three-Dimensional Convolutional Neural Network[J]. Acta Optica Sinica, 2020, 40(20): 2011001.