Depth Transfer by an Imaging System

Streibl, N.

doi:10.1080/713821435

Cited by 65 publications

(22 citation statements)

References 4 publications

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“…Otherwise, an all-over constant magnification can be reached through the orthographic projection of telecentric optics (Watanabe and Nayar, 1997). If that is the case, as well as aforementioned 2-D constraints, the imaging system can be considered as a 3-D shift-invariant linear system (Frieden, 1967;Streibl, 1984) and the 3-D image I can be expressed by the following 3-D convolution (Agard, 1984): Iðx; y; zÞ ¼ Sðx; y; zÞ Ã hðx; y; zÞ; ð2Þ illustrated in Fig. 2.…”

Section: Image Formation Details and Requirementsmentioning

confidence: 99%

Robust 3-D reconstruction of surfaces from image focus by local cross-sectional multivariate statistical analyses: Application to human ex vivo corneal endotheliums

Fernandes¹,

Gavet²,

Pinoli³

2012

Medical Image Analysis

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The considered problem of 3-D reconstruction consists in computationally and passively recovering both topography and texture of a scene surface observed by optical sectioning with a limited depth-of-field imaging system (typically a conventional optical microscope). Throughout a sequence of registered 2-D images, the concepts of shape-from-focus and extended-depth-of-field involve recovering both topography (depth map) and texture image of the surface by researching in-focus information, respectively. Toward that aim, traditional approaches generally follow a 2-D sectional way and thereby fail to deal with noisy and disturbed acquisitions, quite frequent in transmitted light observations and of interest in this paper. Such examples are the acquisitions of human ex vivo corneal endotheliums from the medical issue addressed in this paper, which are mainly damaged by cellular fragments in the sample immersion medium and by emphasized contrast reversals. To achieve with such noisy and disturbed acquisitions, a new focus analysis is introduced that originally adopts a 3-D strategy throughout the image sequence. This method exploits simultaneously all available cross-sectional cues that effectively strengthens the robustness. More precisely, it locally performs multivariate statistical analyses over cross-sectional spatial windows so as to find sectional in-focus positions. Comparisons to state-of-the-art methods on both synthetic data and real acquisitions from the deal-with medical issue demonstrate the efficiency and the robustness of the proposed approach.

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Section: Image Formation Details and Requirementsmentioning

confidence: 99%

Robust 3-D reconstruction of surfaces from image focus by local cross-sectional multivariate statistical analyses: Application to human ex vivo corneal endotheliums

Fernandes¹,

Gavet²,

Pinoli³

2012

Medical Image Analysis

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“…So many properties in microscopic scene are different from those in macroscopic scene, which are as follows [11][12][13]:…”

Section: Light Field Properties In Microscopic Scenementioning

confidence: 99%

Key technologies of light field capture for 3D reconstruction in microscopic scene

Wang

Dai

2010

Sci. China Inf. Sci.

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Light field capture for 3D reconstruction in microscopic scene is a very promising and useful technology, which can be extensively applied to life sciences, medicine, materials science, etc. This paper summarizes the key technologies in the evolution of microscopes for capturing 3D information, including wavefrontreconstruction, holography, fluorescence, tomography and so on. To give in-depth insights into them, detailed analyses and comparisons are provided. Finally, some future potential work in terms of light field capture and its application are discussed at length.

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“…Since an objective lens has the finite numerical aperture (NA), the spatial frequency band is limited in a 3D frequency space, and therefore the sampling distances are defined based on the sarnpling theorem.10) Streibll9) analyzed the spatial frequency band for the diffraction-1imited optical system. Figure 2 shows the 3D optical transfer function (OTF) of a rotational symmetric system, which is calculated according to the analytical result proposed by Streibl,19) in the 2D plane (p, ~' ) , where p represents the radial frequency coordinate parallel to the imaging plane, and Go represents the fre- where ~ represents the wavelength of fluorescence, and F represents the F-number of an objective lens. We will justify these estimations based on the SVD eigenvalue analyses of a C-D image acquisition system: For simulation, we define a pseudo-continuous space actually composed of multiple pixels, where one-pixel length corresponds to 0.0167 ,um.…”

Section: Optimization Of the Sampling Distancesmentioning

confidence: 99%

Microscopic Computed Tomography Based on Generalized Analytic Reconstruction from Discrete Samples

Kikuchi

Matsuya

Yamaguchi

et al. 1996

OPT REV

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We propose a computed tomography (CT) for the incoherent optical system based on a generalized analytic reconstruction method from discrete samples (GARDS), by which an object image defined in a continuous space is reconstructed from discrete images acquired through the imaging system. We apply this method to reconstruct depth structures of micro-specimens using conventional fluorescence microscopes. Also, we discuss the optimal sampling distance in the depth direction through a generalized singular value decomposition of a continuous-to-discrete imaging system. We next apply the GARDS-based CT method to a double-axis microscope, in which two microscopes are set up perpendicularly to cover a missing frequency band of each microscopic imaging system. We show simulation results which verify the effectiveness of our proposed method.

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Depth Transfer by an Imaging System

Cited by 65 publications

References 4 publications

Robust 3-D reconstruction of surfaces from image focus by local cross-sectional multivariate statistical analyses: Application to human ex vivo corneal endotheliums

Robust 3-D reconstruction of surfaces from image focus by local cross-sectional multivariate statistical analyses: Application to human ex vivo corneal endotheliums

Key technologies of light field capture for 3D reconstruction in microscopic scene

Microscopic Computed Tomography Based on Generalized Analytic Reconstruction from Discrete Samples

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