Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introduce and implement a model which combines image-to-image and class-guided denoising diffusion probabilistic models. We train our model on a real-world dataset of microscopy images used for drug discovery, with and without incorporating metadata labels. By exploring the properties of image-to-image diffusion with relevant labels, we show that class-guided image-to-image diffusion can improve the meaningful content of the reconstructed images and outperform the unguided model in useful downstream tasks.

翻译：基于类别的图像对图像扩散: 利用类别标签从亮场图像还原细胞样本 拥有免费或廉价元数据的图像对图像重建问题常常出现在生物和医学图像领域中。现有的基于文本或风格转换的图像对图像方法无法转化为提供离散类别附加信息的数据集。我们提出并实现了一种模型，结合了图像对图像和基于类别引导的去噪扩散概率模型。我们使用真实世界的药物发现显微镜图像数据集进行模型训练，同时考虑类别元数据标签与否。通过探究类别附着下的图像对图像扩散的特性，我们发现基于类别引导的图像对图像扩散能够提高重建图像的实际内容，并在有用的下游任务中优于未指导的模型。