  Natural image matting is a fundamental problem in computational photography
and computer vision. Deep neural networks have seen the surge of successful
methods in natural image matting in recent years. In contrast to traditional
propagation-based matting methods, some top-tier deep image matting approaches
tend to perform propagation in the neural network implicitly. A novel structure
for more direct alpha matte propagation between pixels is in demand. To this
end, this paper presents a hierarchical opacity propagation (HOP) matting
method, where the opacity information is propagated in the neighborhood of each
point at different semantic levels. The hierarchical structure is based on one
global and multiple local propagation blocks. With the HOP structure, every
feature point pair in high-resolution feature maps will be connected based on
the appearance of input image. We further propose a scale-insensitive
positional encoding tailored for image matting to deal with the unfixed size of
input image and introduce the random interpolation augmentation into image
matting. Extensive experiments and ablation study show that HOP matting is
capable of outperforming state-of-the-art matting methods.
