Resfusion: Prior Residual Noise embedded Denoising Diffusion
  Probabilistic Models
Shi Zhenning Dong Changsheng Pan Bin Xie Xueshuo He Along Qu Qiaoying Li Tao
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.