论文标题
瑞士军刀图像到图像翻译：多任务扩散模型
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
论文作者
论文摘要
最近，扩散模型被应用于广泛的图像分析任务。我们以一种使用Denoising扩散隐式模型进行图像到图像翻译的方法构建，并包括回归问题和分割问题，用于将图像生成引导到所需的输出。我们方法的主要优点是，在脱索过程中的指导是由外部梯度完成的。因此，对于同一数据集上的不同任务，不需要对扩散模型进行重新训练。我们使用我们的方法使用回归任务以及大脑磁共振（MR）成像数据集模拟面部照片的老化过程，以模拟脑肿瘤生长。此外，我们使用分割模型将脑部健康切片中所需位置的斑点肿瘤涂成漆。我们为所有问题取得了令人信服的结果。
Recently, diffusion models were applied to a wide range of image analysis tasks. We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output. The main advantage of our approach is that the guidance during the denoising process is done by an external gradient. Consequently, the diffusion model does not need to be retrained for the different tasks on the same dataset. We apply our method to simulate the aging process on facial photos using a regression task, as well as on a brain magnetic resonance (MR) imaging dataset for the simulation of brain tumor growth. Furthermore, we use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images. We achieve convincing results for all problems.
