An image style transfer model based on convolutional neural network (CNN)

Shan Li; Min Li; Xiaoying Li

doi:10.1117/12.3026564

27 March 2024 An image style transfer model based on convolutional neural network (CNN)

Shan Li, Min Li, Xiaoying Li

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050T (2024) https://doi.org/10.1117/12.3026564
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

With the rise of deep learning, the cross collision between artificial intelligence and art, represented by image style transfer, has attracted high attention in the fields of graphic and image technology and art. Based on Convolutional Neural Networks(CNN), this article discusses an image style transfer model. Firstly, define the content loss function and style loss function ,weigh them to calculate the total loss function, incorporate texture features extracted by the Canny operator into the content image; secondly, use the cost function to calculate the values representing content and style for comparison between the initial style and the target style, and then generate a new target image through deformation and combination; finally, train the VGG model to generate the output of the content and style layers of the image, and calculated the corresponding layer parameters applied to the original image for retraining to achieve the minimum loss value. The verification results indicate that adding the Canny operator makes the edges of the style transfer graph more delicate and the contours clearer. This model has a very wide range of applications, such as video processing, photo beautification, social communication, etc. It is currently one of the popular technologies in artificial intelligence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shan Li, Min Li, and Xiaoying Li "An image style transfer model based on convolutional neural network (CNN)", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050T (27 March 2024); https://doi.org/10.1117/12.3026564

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KEYWORDS

Convolutional neural networks

Feature extraction

Image enhancement

Education and training

Edge detection

Image processing

Image segmentation

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