We aim to leverage diffusion to address the challenging image matting task.
However, the presence of high computational overhead and the inconsistency of
noise sampling between the training and inference processes pose significant
obstacles to achieving this goal. In this paper, we present DiffMatte, a
solution designed to effectively overcome these challenges. First, DiffMatte
decouples the decoder from the intricately coupled matting network design,
involving only one lightweight decoder in the iterations of the diffusion
process. With such a strategy, DiffMatte mitigates the growth of computational
overhead as the number of samples increases. Second, we employ a self-aligned
training strategy with uniform time intervals, ensuring a consistent noise
sampling between training and inference across the entire time domain. Our
DiffMatte is designed with flexibility in mind and can seamlessly integrate
into various modern matting architectures. Extensive experimental results
demonstrate that DiffMatte not only reaches the state-of-the-art level on the
Composition-1k test set, surpassing the best methods in the past by 5% and 15%
in the SAD metric and MSE metric respectively, but also show stronger
generalization ability in other benchmarks.

利用扩散方法来解决图像抠像任务，在本论文中，我们提出了 DiffMatte，一种解决方案，旨在有效地克服计算负担和训练与推断过程中噪声采样不一致等挑战。