title: CRYO-ELECTRON MICROSCOPY DATA DENOISING BASED ON THE GENERALIZED DIGITIZED TOTAL VARIATION METHOD.
abstract: The energy functional used in digitalized total variation method is expanded to a general form and a generalized digitized total variation (GDTV) denoising method is obtained. We further expand this method from 2-dimensional (2D) image to 3-dimensional (3D) image processing field. Cryo-electron microscopy (cryo EM) and single particle reconstruction are becoming part of standard collection of structural techniques used for studying macromolecular assemblies. Howerver, the 3D data obtained suffers greatly from noise and degradation for the low dose electron radiation. Thus, image enhancement and noise reduction are theoretically necessary to improve the data for the subsequent segmentation and/or structure skeletonization. Although there are several methods to tackle this problem, we are pleased to find that GDTV method is very efficient and can achieve better performance. Comparative experiments are carried out to verify the enhancement achieved by the GDTV method.
authors.affiliation: Institute for Computational Engineering and Sciences, University of Texas, Austin, TX 78712, U. S. A.
authors.forename: Qin
authors.initials: Q
authors.lastname: Zhang
authors.forename: Chandrajit L
authors.initials: CL
authors.lastname: Bajaj