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Deep learning neural network-based microscopic image three-dimensional reconstruction method and system

A neural network and 3D reconstruction technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problems of difficulty in improving vertical resolution, time-consuming, and ineffective response, so as to improve the speed and resolution of 3D reconstruction, The effect of strong universality and fast imaging speed

Active Publication Date: 2017-06-13
TSINGHUA UNIV
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Problems solved by technology

This image reconstruction method cannot effectively deal with media with changing refractive index or weak scattering, and the longitudinal resolution is not easy to improve, which is restricted by modeling
In addition, this method requires a large number of iterative calculations, which is time-consuming

Method used

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  • Deep learning neural network-based microscopic image three-dimensional reconstruction method and system
  • Deep learning neural network-based microscopic image three-dimensional reconstruction method and system
  • Deep learning neural network-based microscopic image three-dimensional reconstruction method and system

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Embodiment Construction

[0031] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0032] In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", " The orientations or positional relationships indicated by "vertical", "horizontal", "top", "bottom", "inner" and "outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and Simplified descriptions, rather than indicating or implying that the device or element refe...

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Abstract

The invention provides a deep learning neural network-based microscopic image three-dimensional reconstruction method and system. The method comprises the following steps of constructing a neural network; obtaining a training set of the neural network; training the neural network according to the training set, thereby obtaining network parameters; and according to the network parameters, performing three-dimensional reconstruction on a to-be-reconstructed object to obtain a reconstructed image. According to the method and the system, an image recovery reconstruction network is obtained through learning of an optical field image and different layers of focus images, so that the three-dimensional reconstruction speed and resolution are increased, and the longitudinal resolution is greatly increased.

Description

technical field [0001] The invention relates to the technical fields of computational photography and machine learning, and in particular to a method and system for three-dimensional reconstruction of microscopic images based on deep learning neural networks. Background technique [0002] As a new direction in imaging technology, light field imaging can achieve refocusing without mechanical focusing after shooting, but can achieve refocusing through image processing calculations, and can achieve 3D reconstruction and multi-target point focusing. It is widely used in the field of life and microscopic imaging. [0003] Adding a microlens array between the main lens and the imaging surface of a conventional camera or microscope, e.g. figure 1 As shown, the image obtained by using this device is light field imaging, and the camera can simultaneously capture spatial and angular information, that is, the four-dimensional information of the light field. In this way, the light fie...

Claims

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Application Information

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IPC IPC(8): G06T17/00
CPCG06T17/00G06T2207/10052G06T2207/10056G06T2207/20081G06T2207/20084
Inventor 戴琼海周婧雯吴嘉敏
Owner TSINGHUA UNIV
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