无需文本提示，用配对图像实现高分辨率医学影像修复
SANA I2I: A Text Free Flow Matching Framework for Paired Image to Image Translation with a Case Study in Fetal MRI Artifact Reduction
- 纯图像配对训练，基于隐空间流匹配学习图像转换
- 在胎儿MRI中有效抑制运动伪影，少步数下表现优异
- 适合医疗图像修复场景，无需依赖语言描述
我们提出SANA-I2I，一种无文本条件的高分辨率图像到图像生成框架，扩展了SANA系列，完全移除文本控制。与结合文本与图像控制的SanaControlNet不同，SANA-I2I仅依赖配对源-目标图像，在隐空间学习条件流匹配模型。该模型学习一个条件速度场，将目标图像分布映射到另一分布，实现无需语言提示的监督式图像转换。我们在胎儿MRI运动伪影消除这一挑战性任务上评估该方法。为支持此类应用中的配对训练（真实配对数据难获取），我们采用Duffy等人提出的方法，模拟胎儿磁共振成像中的真实运动伪影。实验表明，SANA-I2I能有效抑制运动伪影并保留解剖结构，在极少推理步数下达到具有竞争力的性能。结果凸显了所提基于流、无文本的生成模型在医疗影像监督式图像转换任务中的高效性与适用性。
原文摘要 · Abstract (English)
We propose SANA-I2I, a text-free high-resolution image-to-image generation framework that extends the SANA family by removing textual conditioning entirely. In contrast to SanaControlNet, which combines text and image-based control, SANA-I2I relies exclusively on paired source-target images to learn a conditional flow-matching model in latent space. The model learns a conditional velocity field that maps a target image distribution to another one, enabling supervised image translation without reliance on language prompts. We evaluate the proposed approach on the challenging task of fetal MRI motion artifact reduction. To enable paired training in this application, where real paired data are difficult to acquire, we adopt a synthetic data generation strategy based on the method proposed by Duffy et al., which simulates realistic motion artifacts in fetal magnetic resonance imaging (MRI). Experimental results demonstrate that SANA-I2I effectively suppresses motion artifacts while preserving anatomical structure, achieving competitive performance few inference steps. These results highlight the efficiency and suitability of our proposed flow-based, text-free generative models for supervised image-to-image tasks in medical imaging.
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