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We consider a problem of eye-fundus image reconstruction from different-depth fundus sections based on an image stack, with the images obtained via imaging system's fast refocusing as superposition of 3D object sections and blurred images of adjacent-depth sections. An implicit iterative regularization method in the Fourier plane is used for 3D deconvolution. The results of mathematical modeling have demonstrated that the numerical sectioning shows promise when processing ophthalmological images distorted by various types of noise.

ABSTRACT:In this paper we deal with the problem of optical sectioning. This is a post processing step while investigating of 3D translucent medical objects based on rapid refocusing of the imaging system by the adaptive optics technique. Each image, captured in focal plane, can be represented as the sum of in-focus true section and out-of-focus images of the neighboring sections of the depth that are undesirable in the subsequent reconstruction of 3D object. The problem of optical sectioning under consideration is to elaborate a robust approach capable of obtaining a stack of cross section images purified from such distortions. For a typical sectioning statement arising in ophthalmology we propose a local iterative method in Fourier spectral plane. Compared to the non-local constant parameter selection for the whole spectral domain, the method demonstrates both improved sectioning results and a good level of scalability when implemented on multi-core CPUs.

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