In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring that the final result is consistent with the input image's structures. We also introduce the Alpha Reliability Propagation, a novel technique designed to maximize the utility of available guidance by selectively enhancing the trimap regions with confident alpha information, thus simplifying the correction task. To train the correction module, we devise specialized loss functions that target the accuracy of the alpha matte's edges and the consistency of its opaque and transparent regions. We evaluate our model across several image matting benchmarks, and the results indicate that DiffusionMat consistently outperforms existing methods. Project page at~\url{https://cnnlstm.github.io/DiffusionMat

翻译：在本文中，我们提出DiffusionMat，一种新颖的图像抠图框架，它采用扩散模型实现从粗糙alpha通道到精细alpha通道的过渡。与依赖三分图仅为alpha通道预测提供松散引导的传统方法不同，我们的方法将图像抠图视为序列细化学习过程。该过程从向三分图添加噪声开始，并利用预训练的扩散模型逐步去噪，从而渐进地将预测引导至清晰的alpha通道。我们框架的关键创新在于一个校正模块，该模块在每一步去噪过程中调整输出，确保最终结果与输入图像结构一致。我们还引入Alpha可靠性传播——一种新颖技术，旨在通过选择性增强具有置信alpha信息的三分图区域来最大化可用引导的效用，从而简化校正任务。为训练校正模块，我们设计了专门针对alpha通道边缘精度及其不透明与透明区域一致性的损失函数。我们在多个图像抠图基准上评估模型，结果表明DiffusionMat持续优于现有方法。项目页面：\url{https://cnnlstm.github.io/DiffusionMat}。