Classifier Guided Diffusion for Image Inpainting: Applications to Fine Art

Within the field of Cultural Heritage, image inpainting is a conservation process that fills in missing or damaged parts of an artwork to present a complete image. Multi-modal diffusion models have brought photo-realistic results on image inpainting where content can be generated by using descriptive text prompts. However, these models fail to produce content consistent with the painter's artistic style and period, being unsuitable for the reconstruction of fine arts and requiring laborious expert judgement. This work presents a methodology to improve the inpainting process by automating the selection process. We propose a discriminator model that processes the output of inpainting models and assigns a probability that indicates the likelihood that the restored image belongs to a certain painter.