Jihoon Lee  Chan Ho Bae  Seunghun Lee  Myung-Seok Choi  Ryong Lee  Sangtae Ahn

10.5626/JOK.2024.51.6.537

Image Style Transfer computer vision background inpainting data augmentation Generative Model

Recently, image generation technology has been rapidly advancing in the field of deep learning. One of the most effective ways to represent images is by using text prompts to generate them. The performance of models that generate images using this technique is outstanding. However, it is not easy to naturally change specific parts of an image using only text prompts. This is considered a typical problem with conventional image generation models. Thus, in this study, we developed a background inpainting technique that extracts text for each area of an image and uses it as a basis to seamlessly change the background while preserving the objects in the image. In particular, the background transformation inpainting technique developed in this study has the advantage of not only transforming a single image but also rapidly transforming multiple images. Therefore, the proposed text prompt-based image style transfer can be used in fields with limited data for training, and the technique could enhance the performance of models through image augmentation.