Recently, Denoising Diffusion Probabilistic Models have been widely used in
image segmentation, by generating segmentation masks conditioned on the input
image. However, previous works can not seamlessly integrate existing end-to-end
models with denoising diffusion models. Existing research can only select
acceleration steps based on experience rather than calculating them
specifically. Moreover, most methods are limited to small models and
small-scale datasets, unable to generalize to general datasets and a wider
range of tasks. Therefore, we propose Resfusion with a novel resnoise-diffusion
process, which gradually generates segmentation masks or any type of target
image, seamlessly integrating state-of-the-art end-to-end models and denoising
diffusion models. Resfusion bridges the discrepancy between the likelihood
output and the ground truth output through a Markov process. Through the novel
smooth equivalence transformation in resnoise-diffusion process, we determine
the optimal acceleration step. Experimental results demonstrate that Resfusion
combines the capabilities of existing end-to-end models and denoising diffusion
models, further enhancing performance and achieving outstanding results.
Moreover, Resfusion is not limited to segmentation tasks, it can easily
generalize to any general tasks of image generation and exhibit strong
competitiveness.

最近，通过生成以输入图像为条件的分割掩模，去噪扩散概率模型广泛应用于图像分割。然而，以往的研究无法无缝地将现有端到端模型与去噪扩散模型结合使用。我们提出了一种新的去噪扩散过程，Resfusion，通过马尔可夫过程在似然输出和实际输出之间建立联系，逐步生成分割掩模或任何类型的目标图像，无缝地整合了最先进的端到端模型和去噪扩散模型。实验结果表明，Resfusion 结合了现有端到端模型和去噪扩散模型的能力，进一步提高了性能，取得了出色的结果。此外，Resfusion 不仅限于分割任务，还可以轻松推广到图像生成的任何通用任务，并展现出强大的竞争力。