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.
Year
DOI
Venue
2018
10.1109/GlobalSIP.2018.8646614
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
Cryo-EM images,image denoising,conformational classification,data-driven tight frame
Noise reduction,Dictionary learning,Data-driven,Pattern recognition,Computer science,MOLECULAR CONFORMATIONS,Microscope,Image denoising,Artificial intelligence,Cryo-electron microscopy,Tight frame
Conference
ISSN
ISBN
Citations 
2376-4066
978-1-7281-1295-4
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yin Xian101.69
Hanlin Gu200.34
Wei Wang3131.66
Xuhui Huang4536.96
Yuan Yao559153.27
Yang Wang65910.33
Jian-Feng Cai72828125.44