Name
Title | ||
|---|---|---|
Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy. |
Abstract | ||
|---|---|---|
Due to the limited depth of field of brightfield microscopes, it is usually impossible to image thick specimens entirely in focus. By optically sectioning the specimen, the in-focus information at the specimen's surface can be acquired over a range of images. Commonly based on a high-pass criterion, extended-depth-of-field methods aim at combining the in-focus information from these images into a single image of the texture on the specimen's surface. The topography provided by such methods is usually limited to a map of selected in-focus pixel positions and is inherently discretized along the axial direction, which limits its use for quantitative evaluation. In this paper, we propose a method that jointly estimates the texture and topography of a specimen from a series of brightfield optical sections; it is based on an image formation model that is described by the convolution of a thick specimen model with the microscope's point spread function. The problem is stated as a least-squares minimization where the texture and topography are updated alternately. This method also acts as a deconvolution when the in-focus PSF has a blurring effect, or when the true in-focus position falls in between two optical sections. Comparisons to state-of-the-art algorithms and experimental results demonstrate the potential of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TIP.2008.924393 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
in-focus psf,selected in-focus,extended depth,brightfield optical section,image thick specimen,in- verse problems,deconvolution,optical transfer functions.,index terms—biomedical image processing,single image,brightfield microscopy,in-focus information,image formation model,brightfield microscope,true in-focus position,thick specimen model,image processing,image texture,surface texture,minimisation,algorithms,depth of field,high pass,texture,least square,microscopy,inverse problems,surface topography,convolution,optical microscopy,optical transfer function,indexing terms,image formation,point spread function,lighting,topography,computer simulation | Computer vision,Optical transfer function,Image texture,Image processing,Deconvolution,Image formation,Artificial intelligence,Microscopy,Point spread function,Mathematics,Depth of field | Journal |
Volume | Issue | ISSN |
17 | 7 | 1057-7149 |
Citations | PageRank | References |
23 | 0.95 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François Aguet | 1 | 23 | 0.95 |
| Dimitri Van De Ville | 2 | 153 | 9.80 |
| Unser, M. | 3 | 3438 | 442.40 |