提出高保真布朗桥模型，实现医学图像确定性转换。
Deterministic Medical Image Translation via High-fidelity Brownian Bridges
- 用布朗桥架构构建生成与重建双映射，确保图像可逆。
- 在多模态图像转换和超分辨率任务中超越现有方法。
- 适合需要稳定、高保真医学图像生成的临床研究者。
近期研究表明，扩散模型在生成合成图像方面优于生成对抗网络（GANs）。然而，其输出常因固有随机性而不可控且保真度不足。本文提出一种新型高保真布朗桥模型（HiFi-BBrg），用于确定性医学图像转换。该模型包含两个互惠的映射：生成映射与重建映射。布朗桥训练过程通过重建映射中的保真度损失与对抗训练进行引导，确保转换后的图像能准确还原为原始图像，从而实现一致性高保真转换。在多个数据集上的大量实验表明，HiFi-BBrg 在多模态图像转换与多图像超分辨率任务中均优于当前最优方法。
原文摘要 · Abstract (English)
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.
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