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Electromagnetic inverse scattering imaging method based on perception generative adversarial network

An imaging method and inverse scattering technology, which can be used in biological neural network models, neural learning methods, and radio wave reflection/re-radiation. It can solve the problem of artifacts in reconstructed images, achieve high-quality imaging reconstruction, and improve imaging quality. , predict the effect of accurate

Active Publication Date: 2020-11-27
HEFEI UNIV OF TECH
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Problems solved by technology

If there is no explicit constraint on the features of the target image, it is easy to cause artifacts in the reconstructed image

Method used

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  • Electromagnetic inverse scattering imaging method based on perception generative adversarial network
  • Electromagnetic inverse scattering imaging method based on perception generative adversarial network
  • Electromagnetic inverse scattering imaging method based on perception generative adversarial network

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Embodiment Construction

[0039] In this embodiment, an electromagnetic inverse scatter imaging method based on perceptual generative adversarial networks, first uses the backpropagation method to generate a low-resolution scatterer image x from the measured scattering field, and then uses the generator G θ Mapping from low-resolution images to target images to generate near-real reconstructed images G θ (x). The low-resolution image x generated by BP is used as a condition of the discriminator and the reconstructed image G θ (x) or the real target image y is paired into the discriminator, where the discriminator is used as a feature extractor to extract the features of different hidden layers, so that the reconstructed image and the target image are matched in terms of pixels and features at the same time. Specifically, if figure 1 shown, including the following steps:

[0040] Step 1. Data generation;

[0041] Step 1.1. In a two-dimensional transverse magnetic field, assuming the frequency is 400...

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Abstract

The invention discloses an electromagnetic inverse scattering imaging method based on a perception generative adversarial network, and the method comprises the steps: 1, quickly generating a low-resolution scatterer image through a back propagation method according to a measured scattering field; 2, a network structure building stage: designing a generator and discriminator structure by adopting agenerative adversarial network architecture; 3, a loss function design stage: adding perception confrontation loss into the target function, and extracting the perception confrontation loss by usinga hidden layer of a discriminator, so that the reconstructed image and the target image are matched on pixels and features at the same time; and 4, reconstructing the relative dielectric constant of the scatterer by training the perception generative adversarial network. According to the method, the generation network can learn target feature information more effectively, so that the imaging quality is improved more effectively.

Description

technical field [0001] The invention belongs to the technical field of electromagnetic inverse scattering imaging, and in particular relates to an electromagnetic inverse scattering imaging using a deep learning method. Background technique [0002] Electromagnetic inverse scattering uses the measured scattering field combined with the inversion algorithm to determine the location, shape and physical parameters of the scatterer. Usually electromagnetic inverse scattering is a highly nonlinear and ill-posed problem. After years of development, researchers have proposed various electromagnetic inverse scattering reconstruction algorithms, among which the quantitative method is the mainstream direction of electromagnetic inverse scattering research because it can obtain all the information of the scatterers. [0003] Quantitative methods generally define a nonlinear objective function including regularization terms, and then use global or local linearization approximation to i...

Claims

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Application Information

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IPC IPC(8): G01S13/89G01S7/41G06N3/04G06N3/08
CPCG01S13/89G01S7/418G01S7/417G06N3/084G06N3/048G06N3/045
Inventor 宋仁成黄优优刘羽李畅成娟陈勋
Owner HEFEI UNIV OF TECH
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