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Multi-modal image fusion method based on generative adversarial network and super-resolution network

A multi-modal image, super-resolution technology, applied in the field of image fusion

Inactive Publication Date: 2019-02-12
ZHONGBEI UNIV
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Problems solved by technology

[0005] In order to solve the problem of self-adaptive fusion of multi-modal images, the present invention proposes a multi-modal image fusion method based on generative confrontation network and super-resolution network

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  • Multi-modal image fusion method based on generative adversarial network and super-resolution network
  • Multi-modal image fusion method based on generative adversarial network and super-resolution network
  • Multi-modal image fusion method based on generative adversarial network and super-resolution network

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Embodiment Construction

[0024] A multi-modal image fusion method based on generation confrontation network and super-resolution network, comprising the following steps:

[0025] 1. Design and build a generative confrontation network structure

[0026] The generator network structure is a residual-based convolutional neural network consisting of three convolutional layers and seven residual blocks, each of which contains two convolutional layers; the discriminator consists of six convolutional layers, A standard three-layer residual unit block and a fully connected layer are composed, as follows:

[0027] (1) Input the multi-band source image or multi-modal medical source image into the generator, perform a convolution operation, the convolution kernel size is 3×3×64, and then input 7 residual blocks, each residual block is composed of Consisting of two convolutional layers, the target to be learned is F(x)=H(x)-x, where x represents the network input, H(x) represents the expected output, and F(x) re...

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Abstract

The present invention relates to an image fusion method, in particular to a multimodal image fusion method, especially a multi-modal image fusion method based on a generative adversarial network and asuper-resolution network. The method is carried out according to the following steps of designing and constructing the generative adversarial network, wherein the network structure adopts a conceiveddepth residual neural network, and obtaining a generating model through the dynamic balance training of a generator and a discriminator; constructing the super-resolution network based on the convolution layer; inputting the multi-band / multi-mode source image into the generating model to obtain the preliminary fusion image; and then inputting the image into the trained super-resolution network toget the final fusion image with high quality. The method realizes the end-to-end neural network fusion of multi-band / multi-mode images, avoids the difficulties of image multi-scale and multi-direction decomposition and fusion rule design based on prior knowledge, and realizes the adaptive network fusion.

Description

technical field [0001] The present invention relates to an image fusion method, in particular to a multi-modal image fusion method, specifically a multi-modal image fusion method based on a generation confrontation network and a super-resolution network. Background technique [0002] Multi-band / multi-modal imaging systems have been widely used in military, medical, industrial inspection and many other fields. Image fusion is one of the key technologies for these systems to achieve high-precision intelligent detection. The current image fusion technology can be roughly divided into two categories: air domain and frequency domain. The former algorithm is simple and fast, and is widely used in hardware systems, but the fusion effect is limited; the latter adopts more targeted fusion rules for the multi-scale and multi-directional decomposition results of the original image, which can improve the fusion effect. Current research hotspots, but often the algorithms are complex, re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50G06T3/40G06N3/04
CPCG06T3/4038G06T3/4053G06T5/50G06T2207/10048G06T2207/10088G06T2207/10081G06T2207/10004G06T2207/20081G06T2207/20221G06N3/045
Inventor 蔺素珍杨晓莉李大威王丽芳
Owner ZHONGBEI UNIV
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