标题： 扩散棱镜：增强掩码到图像扩散的多样性与形态一致性 显示英文标题

标题： Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion

摘要： 生成式AI和可控扩散的出现使得图像到图像的合成越来越实用和高效。 然而，当输入图像表现出低熵和稀疏性时，扩散模型的固有特性通常会导致多样性有限。 这种限制显著干扰了数据增强。 为了解决这个问题，我们提出了 Diffusion Prism，一种无需训练的框架，能够在保留形态特征的同时，高效地将二值掩码转换为现实且多样的样本。 我们发现少量的人工噪声会显著有助于图像去噪过程。 为了证明这一新颖的掩码到图像的概念，我们以纳米树突状图案为例，展示我们的方法相比现有可控扩散模型的优势。 此外，我们将所提出的框架扩展到其他生物图案，突出其在各个领域的潜在应用。 显示英文摘要

摘要： The emergence of generative AI and controllable diffusion has made image-to-image synthesis increasingly practical and efficient. However, when input images exhibit low entropy and sparse, the inherent characteristics of diffusion models often result in limited diversity. This constraint significantly interferes with data augmentation. To address this, we propose Diffusion Prism, a training-free framework that efficiently transforms binary masks into realistic and diverse samples while preserving morphological features. We explored that a small amount of artificial noise will significantly assist the image-denoising process. To prove this novel mask-to-image concept, we use nano-dendritic patterns as an example to demonstrate the merit of our method compared to existing controllable diffusion models. Furthermore, we extend the proposed framework to other biological patterns, highlighting its potential applications across various fields.