Local adaptive wiener filtering for class averaging in single particle reconstruction
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings |
| Publisher | Springer Verlag |
| Pages | 233-244 |
| Number of pages | 12 |
| ISBN (Print) | 9783319591285 |
| DOIs | |
| Publication status | Published - 2017 |
| Publication type | A4 Article in a conference publication |
| Event | Scandinavian Conference on Image Analysis - Duration: 1 Jan 1900 → … |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 10270 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Scandinavian Conference on Image Analysis |
|---|---|
| Period | 1/01/00 → … |
Abstract
In cryo-electron microscopy (cryo-EM), the Wiener filter is the optimal operation – in the least-squares sense – of merging a set of aligned low signal-to-noise ratio (SNR) micrographs to obtain a class average image with higher SNR. However, the condition for the optimal behavior of the Wiener filter is that the signal of interest shows stationary characteristic thoroughly, which cannot always be satisfied. In this paper, we propose substituting the conventional Wiener filter, which encompasses the whole image for denoising, with its local adaptive implementation, which denoises the signal locally. We compare our proposed local adaptive Wiener filter (LA-Wiener filter) with the conventional class averaging method using a simulated dataset and an experimental cryo-EM dataset. The visual and numerical analyses of the results indicate that LA-Wiener filter is superior to the conventional approach in single particle reconstruction (SPR) applications.
ASJC Scopus subject areas
Keywords
- Class averaging, Electron microscopy, Local adaptive Wiener filter, Single particle reconstruction, Spectral signal-to-noise ratio