Fig. 2.

The pipeline of CMCMGN is as follows. First, the input is a tensor that stacks the background and multi-confidence masks into a single tensor to serve. We then employ two identical Denoising Diffusion Probabilistic Models (DDPM) to optimize our results. Specifically, during the optimization phase, we use the generated result as input for one DDPM and add Gaussian noise to this result, which then serves as the input for the second DDPM. This process yields two outputs, referred to as and . Finally, we utilize the grayscale information from to remap , producing the final results