VQBB: Image-to-image Translation with Vector Quantized Brownian Bridge

Image-to-image translation is an important and challenging problem in computer vision. Existing approaches like Pixel2Pixel, DualGAN suffer from the instability of GAN and fail to generate diverse outputs because they model the task as a one-to-one mapping. Although diffusion models can generate images with high quality and diversity, current conditional diffusion models still can not maintain high similarity with the condition image on image-to-image translation tasks due to the Gaussian noise added in the reverse process. To address these issues, a novel Vector Quantized Brownian Bridge(VQBB) diffusion model is proposed in this paper. On one hand, Brownian Bridge diffusion process can model the transformation between two domains more accurate and flexible than the existing Markov diffusion methods. As far as the authors know, it is the first work for Brownian Bridge diffusion process proposed for image-to-image translation. On the other hand, the proposed method improved the learning efficiency and translation accuracy by confining the diffusion process in the quantized latent space. Finally, numerical experimental results validated the performance of the proposed method.