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.

中文翻译：

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图像抠图的分层不透明度传播

自然图像抠图是计算摄影和计算机视觉中的一个基本问题。近年来，深度神经网络见证了自然图像抠图的成功方法激增。与传统的基于传播的抠图方法相比，一些顶级的深度图像抠图方法倾向于在神经网络中隐式地执行传播。需要一种用于像素之间更直接的 alpha 遮罩传播的新颖结构。为此，本文提出了一种分层不透明度传播（HOP）抠图方法，其中不透明度信息在不同语义级别的每个点的邻域中传播。层次结构基于一个全局和多个局部传播块。采用 HOP 结构，高分辨率特征图中的每个特征点对都将根据输入图像的外观进行连接。我们进一步提出了一种为图像抠图量身定制的尺度不敏感位置编码，以处理输入图像的不固定大小，并将随机插值增强引入图像抠图。大量的实验和消融研究表明 HOP 消光能够胜过最先进的消光方法。