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An image style transfer method based on convolutional neural network

A technology of convolutional neural network and transfer method, which is applied in the direction of graphic image conversion, image data processing, 2D image generation, etc.

Active Publication Date: 2019-08-13
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
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Problems solved by technology

[0004] In view of the above existing problems or deficiencies, in order to solve the problem of efficiently performing style transfer on any style image, the present invention provides an image style transfer method based on convolutional neural network

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  • An image style transfer method based on convolutional neural network
  • An image style transfer method based on convolutional neural network
  • An image style transfer method based on convolutional neural network

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Embodiment Construction

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0026] figure 2 is the target content image, image 3 for the target style image. Our goal is to generate image 4 such that it fuses figure 2 the content of and image 3 style of.

[0027] Step 1. Select the deep convolutional neural network VGG-19, which achieved excellent results in the ImageNet image classification competition in 2014, as our image advanced semantic feature extraction model Φ, and select figure 2 for target content image X C , image 3 For target style image X S , select ReLU2_2 as the content constraint layer, select ReLU1_1, ReLU2_1, ReLU3_1, ReLU4_1 and ReLU5_1 as the style constraint layer, and select the setting threshold ε=5e -3 and the highest number of iterations th = 200;

[0028] Step 2, the target content image X C Input to the convolutional neural network VGG-19 to calculate the filter response Φ(X ...

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Abstract

The invention belongs to the fields of image processing and computer vision, and specifically relates to an image style transfer method based on a convolutional neural network. Based on the high-level semantic representation in the convolutional neural network, the image content model and the image style model are established, and then an initial image is optimized to have a similar content representation to the content image and a similar style image to the same convolutional neural network. Style representation, so as to generate an image that fuses the content of the content image and the style of the style image, and realizes the style transfer function. The invention can realize style transfer for any style image.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, relates to related technologies such as deep learning and image generation, and specifically relates to an image style transfer method based on a convolutional neural network. Background technique [0002] In daily life, whether it is taking pictures or painting, people often hope to make it have a certain style through post-editing. However, image editing and painting require high skills and rich experience, and it is difficult for ordinary people to realize the function of style transfer without learning. [0003] The current existing image style transfer methods are mainly realized by non-parametric algorithms. These methods can effectively transfer the texture primitive structure such as color and thin edge of the style image to the content image. However, these methods can only extract the low-level semantic features of the image and realize the primary style transfer of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/00G06T11/00
CPCG06T3/0012G06T11/001
Inventor 朱策夏志强向俊曌文宏雕虢齐王征韬
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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