BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models

Image-to-image translation is an important and challenging problem in computer vision and image processing. Diffusion models (DM) have shown great potentials for high-quality image synthesis, and have gained competitive performance on the task of image-to-image translation. However, most of the existing diffusion models treat image-to-image translation as conditional generation processes, and suffer heavily from the gap between distinct domains. In this paper, a novel image-to-image translation method based on the Brownian Bridge Diffusion Model (BBDM) is proposed, which models image-to-image translation as a stochastic Brownian Bridge process, and learns the translation between two domains directly through…

• NN
  National Natural Science Foundation of China

  Award: 62172198,61762064
• NS
  Natural Science Foundation of Jiangxi Province

  Award: 20224ACB202008