Image Style Transfer
Image style transfer aims to blend the content of one image with the style of another, creating novel artistic or visually enhanced outputs. Current research emphasizes efficient algorithms, including those based on pixel shuffling and diffusion models, and explores diverse input modalities such as images and text descriptions to guide the style transfer process. This field is significant for its applications in art, image editing, and medical imaging, offering tools for creative expression and improved data analysis. Furthermore, ongoing work focuses on improving control over style application, enhancing content preservation, and developing methods that require less training data.
Papers
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams, Elizabeth Mouchet, Yinhai Wang, Carola-Bibiane Schönlieb
Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer
Serin Yang, Hyunmin Hwang, Jong Chul Ye