Deblurring of Class-Averaged Images in Single-Particle Electron Microscopy.

ABSTRACT: This paper proposes a method for deblurring of class-averaged images in single-particle electron microscopy (EM). Since EM images of biological samples are very noisy, the images which are nominally identical projection images are often grouped, aligned and averaged in order to cancel or reduce the background noise. However, the noise in the individual EM images generates errors in the alignment process, which creates an inherent limit on the accuracy of the resulting class averages. This inaccurate class average due to the alignment errors can be viewed as the result of a convolution of an underlying clear image with a blurring function. In this work, we develop a deconvolution method that gives an estimate for the underlying clear image from a blurred class-averaged image using precomput

SUBMITTER: Park W 

PROVIDER: S-EPMC2835172 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature