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Cellular nerve network with genetic algorithm (GACNN)-based multisource image fusion method

A neural network and genetic optimization technology, applied in biological neural network models, image data processing, graphics and image conversion, etc., can solve problems such as unused image fusion

Active Publication Date: 2014-08-06
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

The use of genetic algorithms to calculate the template parameters of cellular neural networks was proposed by Chandler, Rekeczky B, Nishio C, and Ushida Y (1996). Domestic scholars also published related articles in 2001, but this idea has not been used for image fusion. among

Method used

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  • Cellular nerve network with genetic algorithm (GACNN)-based multisource image fusion method
  • Cellular nerve network with genetic algorithm (GACNN)-based multisource image fusion method
  • Cellular nerve network with genetic algorithm (GACNN)-based multisource image fusion method

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Embodiment

[0064] ① Read in the original source image, and implement mapping processing on the preprocessed image, so that the values ​​of all pixels are normalized between [-1.0, +1.0], which meets the input requirements of the CNN network, and image gray value mapping processing The formula is as follows:

[0065] u ij =1-2g ij / 255, where g ij Indicates the pixel value of row i and column j of the source image. (1)

[0066] ②According to the size of the input image, determine the size of the cell neighborhood of the network, thereby determining the structure of the network. In this embodiment, the size of the neighborhood of the cells used is 3×3. According to the discrete state equation of the fusion processing of the static image :

[0067] x ij ( n ) - x ij ( n - 1 ) =...

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Abstract

The invention discloses a cellular nerve network with genetic algorithm (GACNN)-based multisource image fusion method, belonging to the field of multisource mage fusion. According to the GACNN-based multisource image fusion method, the multisource image in the same scene can output fusion images with better effect by taking a CNN system as a framework and combining genetic algorithm self-adaptive computing network template parameters, thus facilitating the follow-up processing of image information, and providing more useful and more efficient information for the aspects of feature extraction, image recognition, human decision and the like; the accuracy of the fusion image can be effectively improved while the fusion result can be fast obtained, thus facilitating the observation of human eyes and machine detection, and being convenient for analysis and practical application.

Description

technical field [0001] The present invention relates to the field of multi-source image fusion, in particular to a multi-source image fusion method based on a genetically optimized cellular neural network (Cellular Neural Networks with genetic algorithm, GACNN). The parameterized cellular neural network (CNN) outputs a fusion image with a good effect. A good fusion image is convenient for the subsequent processing of image information, such as feature extraction, image recognition, and human decision-making. It provides more useful and efficient information, and has a good Practical value. Background technique [0002] With the rapid development of image processing technology, for the same scene, the image information acquired by imaging sensors with different physical characteristics is very different. Comprehensive analysis of these information is conducive to improving the utilization of image information. Multi-source image fusion technology is a technology that can eff...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06N3/02G06N3/12
Inventor 彭真明李江阳魏瑞鹏黄振星李全忠胡丽华张帆
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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