ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning

no code implementations • 4 Dec 2023 • Shi Zhenning, Dong Changsheng, Xie Xueshuo, Pan Bin, He Along, Li Tao

Rather than using denoising diffusion probabilistic models alone, integrating the abilities of both denoising diffusion probabilistic models and existing end-to-end models can better improve the performance of image segmentation.

Denoising Ensemble Learning +4

Resfusion: Prior Residual Noise embedded Denoising Diffusion Probabilistic Models

no code implementations • 25 Nov 2023 • Shi Zhenning, Dong Changsheng, Pan Bin, Xie Xueshuo, He Along, Qu Qiaoying, Li Tao

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.

Denoising Image Generation +3