Image integration method and system based on convolution neural network model

A convolutional neural network and image fusion technology, applied in biological neural network models, neural architecture, image enhancement, etc., can solve problems such as algorithm complexity, achieve the effects of improving efficiency, strong adaptability, and enriching user experience

Inactive Publication Date: 2017-10-10
GUANGZHOU INTELLIGENT CITY DEV INST +1
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Problems solved by technology

[0003] However, the existing image fusion technology is not perfect, and the algorithm complexity, effectiveness, and time consumption are still at a level that urgently needs to be optimized.
The huge user demand needs more

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  • Image integration method and system based on convolution neural network model
  • Image integration method and system based on convolution neural network model
  • Image integration method and system based on convolution neural network model

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[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0043] figure 1 It is a schematic flow diagram of the image fusion method based on the convolutional neural network model in the embodiment of the present invention, such as figure 1 Said image fusion method includes:

[0044] S11: Obtain at least one piece of style image information to be fused and at least one piece of content image information to be fused, and scale the style image information to be fused and the content image informatio...

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Abstract

The invention discloses an image integration method and a system based on a convolution neural network model, wherein the image integration method comprises: obtaining the image information of at least one style-to-be-integrated image and the image information of at least one content-to-be-integrated image; scaling the style-to-be-integrated image and the content-to-be-integrated image to a unified size; performing initial integration to the size unified style-to-be-integrated image and content-to-be-integrated image to obtain the initially integrated image information; calculating in the convolution neural network the information loss gradients between the initially integrated image and the size unified style-to-be-integrated image and content-to-be-integrated image to obtain the overall loss gradient; and based on the overall loss gradient, updating the initially integrated image information and storing the parameters for the convolution neural network model. The embodiments of the invention meet the user's requirements for image integration and increase the image integration speed.

Description

technical field [0001] The present invention relates to image processing technology, in particular to an image fusion method and system based on a convolutional neural network model. Background technique [0002] In today's society, as mobile phone photography becomes more and more popular, various post-image processing software is prevalent, and the demand for image fusion technology such as image editing provided by the software is also expanding. A fast, effective, and interesting Advanced image fusion technology has become the goal pursued by this type of industry, so as to further attract more users to use their own products. [0003] However, the existing image fusion technology is not perfect, and the algorithm complexity, effectiveness, and time consumption are still at a level that urgently needs to be optimized. The huge user demand needs more complete and rich technical support to be filled, but the current technical model cannot complete the fusion of pictures i...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06N3/04
CPCG06T5/50G06T2207/20221G06T2207/20084G06N3/045
Inventor 胡建国商家煜黄俊威李仕仁梁津铨
Owner GUANGZHOU INTELLIGENT CITY DEV INST
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