Lightweight Image Matting via Efficient Non-local Guidance.

Natural image matting aims to estimate the opacity of foreground objects. Most existing approaches involve prohibitive parameters, daunting computational complexity, and redundant dependency. In this paper, we propose a lightweight matting method termed LiteMatting, which learns the local smoothness of color space and affinities between neighboring pixels to estimate the alpha mattes. Specifically, a modified mobile block is adopted to construct an encoder-decoder framework, which reduces parameters while retaining sufficient spatial and channel information. In addition, a Long-Short Range Pyramid Pooling Module (LSRPPM) is introduced to extend the reception field by capturing long-range dependency between regions distributed discretely. Finally, an Efficient Non-Local Block (ENB) is presented for guiding high-level semantics propagation from low-level detail features to refine the alpha mattes. Extensive experiments demonstrate that our method achieves a favorable trade-off between accuracy and efficiency. Compared with most state-of-the-art approaches, our method attains an immense descent in parameters and FLOPs with 30 $$\%$$ and 13 $$\%$$ , respectively, while achieving an improvement of over 15 $$\%$$ in SAD metrics. Code and model are available at https://github.com/kzx2018/LiteMatting.