发表机构
Tel Aviv University（特拉维夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PRISM是一种无GAN的流匹配框架，通过分布门控实现可控非配对图像翻译，在5个自然与生物医学基准上，其在多数任务的Inception FID、KID及组织病理学细胞核计数比上表现优异，平衡了目标逼真度与结构保留。
AI 中文摘要
非配对图像到图像翻译需在无配对监督的情况下，为每张图像决定要更改和保留的内容。许多基于扩散的非配对翻译器通过应用于整张图像的单一全局噪声或引导值来控制保留内容，无法区分需保留的内容与需更改的外观。本文提出PRISM，一种无GAN的流匹配框架，用学习得到的逐特征门控替代该全局控制。该门控的空间先验源自每个源特征与目标特征分布的标准化距离，因此远离目标的特征可被释放，与目标一致的特征则被保留。该门控同时控制初始化（将真实源潜变量与任务匹配的损坏混合）和常微分方程（ODE）积分期间的传输时序。损坏与任务匹配：对于结构保留翻译采用内容锚定的AdaIN，对于结构变化翻译采用部分锚定。推理时可从文本或检测器局部覆盖该门控，无需重新训练，在保留原始图像重要结构的同时仍能生成逼真结果。我们在5个自然和生物医学基准（AFHQ猫→狗、CelebA-HQ外观翻译、白天→夜晚重照明、虚拟染色及乳腺冷冻→永久组织病理学）上评估PRISM。在相同拆分协议下，所评估方法中，PRISM在4个基准上取得最佳Inception FID和KID，在第5个基准上取得有竞争力的结果，且在组织病理学任务中，其细胞核计数比最接近理想值，支持在目标逼真度与结构保留间实现良好平衡。
英文摘要
Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision. Many diffusion-based unpaired translators control preservation through a single global noise or guidance value applied across the image, which cannot separate content to keep from appearance to change. We present PRISM, a GAN-free flow-matching framework that replaces this global control with a learned per-feature gate. The gate's spatial prior is derived from each source feature's standardized distance to the target feature distribution, so features far from the target are freed while target-consistent features are preserved. The same gate controls both the initialization, which mixes the real source latent with a task-matched corruption, and the transport timing during Ordinary Differential Equation (ODE) integration. The corruption is matched to the task, content-anchored (AdaIN) for structure-preserving translation and partially anchored for structure-changing translation, and the gate can be overridden locally at inference from text or a detector without retraining, preserving important structures of the original image while still generating realistic results. We evaluate PRISM on five natural and biomedical benchmarks (AFHQ cat->dog, CelebA-HQ appearance translation, day->night relighting, virtual staining, and breast frozen->permanent histopathology). Among the evaluated methods under a shared same-split protocol, PRISM attains the best Inception FID and KID on four benchmarks and a competitive result on the fifth, and on histopathology yields the nuclei-count ratio closest to the ideal, supporting a favorable balance between target realism and structural preservation.
