A new approach to the deconvolution and filtering of 3-D microscopy images is introduced in this paper. A state-space representation of the image is derived according to the assumption that the whole image can be modelled by an ensemble of smooth 3-D Gaussian random fields. Blurring and noise are then easily included in the representation. Making use of this model the image restoration is carried out by means of a Kalman-based minimum variance estimation algorithm. The reported simulation results show high performances of the proposed approach.

A New Approach for Deconvolution and Filtering of 3-D Microscopy Images

Conte, F.
;
Iannello, G.
2011-01-01

Abstract

A new approach to the deconvolution and filtering of 3-D microscopy images is introduced in this paper. A state-space representation of the image is derived according to the assumption that the whole image can be modelled by an ensemble of smooth 3-D Gaussian random fields. Blurring and noise are then easily included in the representation. Making use of this model the image restoration is carried out by means of a Kalman-based minimum variance estimation algorithm. The reported simulation results show high performances of the proposed approach.
2011
978-3-902661-93-7
Image modelling, Deconvolution, Image restoration, Optimal filtering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/15689
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