Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational  Classification

Yin Xian; Hanlin Gu; Wei Wang; Xuhui Huang; Yuan Yao; Yang Wang; Jian-Feng Cai

doi:10.1109/globalsip.2018.8646614

Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification

Yin Xian, Hanlin Gu, Wei Wang, Xuhui Huang, Yuan Yao, Yang Wang, Jian-Feng Cai

School of Continuing Education

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

Abstract

The cryo-electron microscope (cryo-EM) is increasingly popular these years. It helps to uncover the biological structures and functions of macromolecules. In this paper, we address image denoising problem in cryo-EM. Denoising the cryo-EM images can help to distinguish different molecular conformations and improve three dimensional reconstruction resolution. We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF algorithm is closely related to the dictionary learning. The advantage of DDTF algorithm is that it is computationally efficient, and can well identify the texture and shape of images without using large data samples. Experimental results on cryo-EM image denoising and conformational classification demonstrate the power of DDTF algorithm for cryo-EM image denoising and classification.

Original language	English
Title of host publication	2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Publisher	IEEE
Pages	544-548
Number of pages	5
ISBN (Electronic)	9781728112954, 9781728112947
ISBN (Print)	9781728112961
DOIs	https://doi.org/10.1109/globalsip.2018.8646614
Publication status	Published - Nov 2018
Event	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States Duration: 26 Nov 2018 → 29 Nov 2018 https://ieeexplore.ieee.org/xpl/conhome/8637665/proceeding

Publication series

Name	IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Conference

Conference	2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/Territory	United States
City	Anaheim
Period	26/11/18 → 29/11/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8637665/proceeding

User-Defined Keywords

Cryo-EM images
image denoising
conformational classification
data-driven tight frame

Access to Document

10.1109/globalsip.2018.8646614

Cite this

Xian, Y., Gu, H., Wang, W., Huang, X., Yao, Y., Wang, Y., & Cai, J.-F. (2018). Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 544-548). (IEEE Global Conference on Signal and Information Processing (GlobalSIP)). IEEE. https://doi.org/10.1109/globalsip.2018.8646614

@inproceedings{4bc8f85dbfc24d0c81d59d9a06374f7d,

title = "Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification",

abstract = "The cryo-electron microscope (cryo-EM) is increasingly popular these years. It helps to uncover the biological structures and functions of macromolecules. In this paper, we address image denoising problem in cryo-EM. Denoising the cryo-EM images can help to distinguish different molecular conformations and improve three dimensional reconstruction resolution. We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF algorithm is closely related to the dictionary learning. The advantage of DDTF algorithm is that it is computationally efficient, and can well identify the texture and shape of images without using large data samples. Experimental results on cryo-EM image denoising and conformational classification demonstrate the power of DDTF algorithm for cryo-EM image denoising and classification.",

keywords = "Cryo-EM images, image denoising, conformational classification, data-driven tight frame",

author = "Yin Xian and Hanlin Gu and Wei Wang and Xuhui Huang and Yuan Yao and Yang Wang and Jian-Feng Cai",

year = "2018",

month = nov,

doi = "10.1109/globalsip.2018.8646614",

language = "English",

isbn = "9781728112961",

series = "IEEE Global Conference on Signal and Information Processing (GlobalSIP)",

publisher = "IEEE",

pages = "544--548",

booktitle = "2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)",

address = "United States",

note = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",

url = "https://ieeexplore.ieee.org/xpl/conhome/8637665/proceeding",

}

Xian, Y, Gu, H, Wang, W, Huang, X, Yao, Y, Wang, Y & Cai, J-F 2018, Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification. in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE, pp. 544-548, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, Anaheim, California, United States, 26/11/18. https://doi.org/10.1109/globalsip.2018.8646614

Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification. / Xian, Yin; Gu, Hanlin; Wang, Wei et al.
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. p. 544-548 (IEEE Global Conference on Signal and Information Processing (GlobalSIP)).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification

AU - Xian, Yin

AU - Gu, Hanlin

AU - Wang, Wei

AU - Huang, Xuhui

AU - Yao, Yuan

AU - Wang, Yang

AU - Cai, Jian-Feng

PY - 2018/11

Y1 - 2018/11

N2 - The cryo-electron microscope (cryo-EM) is increasingly popular these years. It helps to uncover the biological structures and functions of macromolecules. In this paper, we address image denoising problem in cryo-EM. Denoising the cryo-EM images can help to distinguish different molecular conformations and improve three dimensional reconstruction resolution. We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF algorithm is closely related to the dictionary learning. The advantage of DDTF algorithm is that it is computationally efficient, and can well identify the texture and shape of images without using large data samples. Experimental results on cryo-EM image denoising and conformational classification demonstrate the power of DDTF algorithm for cryo-EM image denoising and classification.

AB - The cryo-electron microscope (cryo-EM) is increasingly popular these years. It helps to uncover the biological structures and functions of macromolecules. In this paper, we address image denoising problem in cryo-EM. Denoising the cryo-EM images can help to distinguish different molecular conformations and improve three dimensional reconstruction resolution. We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF algorithm is closely related to the dictionary learning. The advantage of DDTF algorithm is that it is computationally efficient, and can well identify the texture and shape of images without using large data samples. Experimental results on cryo-EM image denoising and conformational classification demonstrate the power of DDTF algorithm for cryo-EM image denoising and classification.

KW - Cryo-EM images

KW - image denoising

KW - conformational classification

KW - data-driven tight frame

U2 - 10.1109/globalsip.2018.8646614

DO - 10.1109/globalsip.2018.8646614

M3 - Conference proceeding

SN - 9781728112961

T3 - IEEE Global Conference on Signal and Information Processing (GlobalSIP)

SP - 544

EP - 548

BT - 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

PB - IEEE

T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018

Y2 - 26 November 2018 through 29 November 2018

ER -