Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy

SZU-CHI CHUNG INSTITUTE OF STATISTIC SHAO-HSUAN WANG INSTITUTE OF STATISTIC PO-YAO NIU INSTITUTE OF STATISTIC SU-YUN HUANG INSTITUTE OF STATISTIC WEI-HAU CHANG INSTITUTE OF STATISTIC I-PING TU INSTITUTE OF STATISTIC

Data Analysis mathscidoc:2005.84001

Silver Award Paper in 2020

Annals of Mathematical Sciences and Applicaitons , 5, (2), 2020
Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem for the sample covariance matrix. We propose herein a two-stage dimension reduction (2SDR) method for image reconstruction from high-dimensional noisy image data. The first stage treats the image as a matrix, which is a tensor of order 2, and uses multilinear principal component analysis (MPCA) for matrix rank reduction and image denoising. The second stage vectorizes the reduced-rank matrix and achieves further dimension and noise reduction. Simulation studies demonstrate excellent performance of 2SDR, for which we also develop an asymptotic theory that establishes consistency of its rank selection. Applications to cryo-EM (cryogenic electronic microscopy), which has revolutionized structural biology, organic and medical chemistry, cellular and molecular physiology in the past decade, are also provided and illustrated with benchmark cryo-EM datasets. Connections to other contemporaneous developments in image reconstruction and high-dimensional statistical inference are also discussed.
Generalized Information Criterion, Image Denoising and Reconstruction, Random Matrix Theory, Rank Selection, Stein’s Unbiased Estimate of Risk.
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@inproceedings{szu-chi2020two-stage,
  title={Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy},
  author={SZU-CHI CHUNG, SHAO-HSUAN WANG, PO-YAO NIU, SU-YUN HUANG, WEI-HAU CHANG, and I-PING TU},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20200522175957587166691},
  booktitle={Annals of Mathematical Sciences and Applicaitons },
  volume={5},
  number={2},
  year={2020},
}
SZU-CHI CHUNG, SHAO-HSUAN WANG, PO-YAO NIU, SU-YUN HUANG, WEI-HAU CHANG, and I-PING TU. Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy. 2020. Vol. 5. In Annals of Mathematical Sciences and Applicaitons . http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20200522175957587166691.
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