Research and implementation of fast image style transfer

Yi Zhang

doi:10.1117/12.3008532

14 November 2023 Research and implementation of fast image style transfer

Yi Zhang

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129340Q (2023) https://doi.org/10.1117/12.3008532
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Image style transfer refers to the process of merging the content of a source image with the style(s) of one or more reference images, thereby creating images that combine the original content with other styles. This dissertation focuses on using a convolutional neural network (CNN) to achieve this goal. The image style transfer is completed with data augmentation, a loss network, and an image transformation network. The VGG-19 network is used to extract features in the loss network, and the content loss function and style are optimized iteratively through gradient descent. Additionally, a custom residual module network is trained to enable a specific style conversion of the image. As a result, the final model shows significant improvement, with the final style loss reduced to 2000E+4, and the total loss reduced to 6000E+4, thus achieving good results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Zhang "Research and implementation of fast image style transfer", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129340Q (14 November 2023); https://doi.org/10.1117/12.3008532

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KEYWORDS

Image processing

Batch normalization

Convolution

Feature extraction

Artificial neural networks

Mathematical optimization

Convolutional neural networks

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