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Model-Based 2.5-D Deconvolution for Extended Depth of Field in Brightfield Microscopy

F. Aguet, D. Van De Ville, M. Unser

IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1144-1153, July 2008.

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.

AUTHOR="Aguet, F. and Van De Ville, D. and Unser, M.",
TITLE="Model-Based {2.5-D} Deconvolution for Extended Depth of Field in
        Brightfield Microscopy",
JOURNAL="{IEEE} Transactions on Image Processing",

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