Transforming images into paintings in the style of Van Gogh based on CycleGAN

Ming Wu

doi:10.1117/12.2655883

12 January 2023 Transforming images into paintings in the style of Van Gogh based on CycleGAN

Ming Wu

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125091B (2023) https://doi.org/10.1117/12.2655883
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Nowadays, with the rapid development of entertainment industry, filter has almost become an indispensable function in photography. However, most of the filters today are not directly change the style of an image, and even those techniques that can achieve image style transfer are difficult to be applied by users of other disciplines because of the complexity of their algorithms. In this study, a Generative Adversarial Network named CycleGAN, is used to transfer normal landscape images to images with the style of Van Gogh’s paintings. By using this model, two generators and discriminators will be formed. For the generator, they both generate a domain of images to the other domain, transfer them back to the original domain, and to make sure the generated images will not be differed so much with the original images. In this way, the generator can learn the important features of a specific style of images and apply them to another set of images with a different style. Experimental results show that the model does well in transferring normal landscape images to images with the style of Van Gogh’s paintings, but for the transformation from the style of Van Gogh’s paintings to landscape images, the effect of the model is not very ideal.

Citation Download Citation

Ming Wu "Transforming images into paintings in the style of Van Gogh based on CycleGAN", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125091B (12 January 2023); https://doi.org/10.1117/12.2655883

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