Image processing method and device based on neural network

A neural network and image processing technology, applied in the computer field, can solve the problems of increasing the complexity of neural network training, increasing the complexity of neural network training, and being difficult to apply to practical applications, achieving strong scalability, increasing training complexity, easy-to-achieve effects
CN106548207BActive Publication Date: 2018-11-30BEIJING TUSEN WEILAI TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING TUSEN WEILAI TECH CO LTD
Publication Date
2018-11-30

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Abstract

The invention discloses an image processing method and device based on a neural network to solve the problem of low image processing accuracy in the prior art. The method includes: receiving an image to be processed; using a preset neural network including at least one transformation layer as a factorized bilinear layer, processing the image to be processed to obtain a processing result; wherein, the factorized bilinear layer The output of each neuron in is the sum of the linear linear term of the input feature vector of the neuron and the factorized quadratic term representing the correlation between the input feature vectors of the neuron. In the technical solution of the present invention, the factorization quadratic item representing the correlation of the input feature vector of the neuron is added in the output result of the neuron, and the expressive ability of the neural network is improved, and the image is processed by using the neural network with strong expressive ability , which improves the accuracy of image processing.
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Description

technical field

[0001] The invention relates to the field of computers, in particular to an image processing method and device based on a neural network. Background technique

[0002] Due to the superiority of neural networks, it is more and more common to use neural networks for image processing in the computer field. Currently, commonly used neural networks include GoogleNet, VGG, ResNet, etc. These neural networks are usually stacked by multiple basic units. The unit consists of a linear convolution layer and a nonlinear activation layer (activation functions such as tanh, sigmoid, relu, etc.), these neural networks have the ability to perform complex modeling and feature extraction on images.

[0003] However, since the convolutional layers contained in the basic units of these neural networks are linear convolutional layers, the expressive ability of neural networks is limited to a certain extent. However, in practical applications, the patterns in the image are more c...

Claims

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