SCALE-ADAPTIVE INVERSE IN 3 D IMAGING

In this paper we propose a novel nonparametric approach to reconstruction of three-dimensional (3D) objects from 2D blurred and noisy observations. The proposed technique is based on an approximate image formation model which takes into account depth varying nature of the blur described by the point-spread function (PSF ) of an optical system. The shift-invariantness of PSF in the depth is not required in the considered model. It is more general approach then typical 3D deconvolution problem with a shift-invariant PSF in all dimensions. The proposed restoration scheme incorporates regularized inverse and regularized Wiener inverse filters in combination with an adaptive denoising technique. The simulation results show efficiency of the developed approach.

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