论文标题
BBDM：带有布朗桥扩散模型的图像到图像翻译
BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models
论文作者
论文摘要
图像到图像翻译是计算机视觉和图像处理中的重要且具有挑战性的问题。扩散模型（DM）表现出了高质量图像合成的巨大潜力，并在图像到图像翻译的任务上获得了竞争性能。但是，大多数现有的扩散模型将图像到图像翻译视为有条件的生成过程，并因不同域之间的差距而遭受重大损失。在本文中，提出了一种基于布朗桥扩散模型（BBDM）的新型图像到图像翻译方法，该方法被提出，该方法将图像到图像翻译模拟为随机的布朗桥工艺，并通过双向扩散过程直接了解两个域之间的翻译，而不是条件产生。据我们所知，这是第一项提出图像到图像翻译的布朗桥扩散过程的工作。对各种基准测试的实验结果表明，提出的BBDM模型通过视觉检查和可测量的指标来实现竞争性能。
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 the bidirectional diffusion process rather than a conditional generation process. To the best of our knowledge, it is the first work that proposes Brownian Bridge diffusion process for image-to-image translation. Experimental results on various benchmarks demonstrate that the proposed BBDM model achieves competitive performance through both visual inspection and measurable metrics.
