  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
