Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-deterministic and lack high fidelity to the ground truth due to the inherent randomness. In this paper, we propose a novel High-fidelity Brownian bridge model (HiFi-BBrg) for deterministic medical image translations. Our model comprises two distinct yet mutually beneficial mappings: a generation mapping and a reconstruction mapping. The Brownian bridge training process is guided by the fidelity loss and adversarial training in the reconstruction mapping. This ensures that translated images can be accurately reversed to their original forms, thereby achieving consistent translations with high fidelity to the ground truth. Our extensive experiments on multiple datasets show HiFi-BBrg outperforms state-of-the-art methods in multi-modal image translation and multi-image super-resolution.

翻译：近期研究表明，与生成对抗网络（GANs）相比，扩散模型能生成更优的合成图像。然而，由于固有的随机性，其输出常具有非确定性且缺乏对真实数据的高保真度。本文提出一种用于确定性医学图像翻译的新型高保真布朗桥模型（HiFi-BBrg）。该模型包含两个相互独立又彼此促进的映射：生成映射与重构映射。布朗桥训练过程通过重构映射中的保真度损失与对抗训练进行引导，确保翻译后的图像能精确还原至原始形态，从而实现与真实数据高度一致的确定性翻译。我们在多个数据集上的大量实验表明，HiFi-BBrg在多模态图像翻译与多图像超分辨率任务中均优于现有最优方法。