  This paper reviews recent deep-learning-based matting research and conceives
our wider and higher motivation for image matting. Many approaches achieve
alpha mattes with complex encoders to extract robust semantics, then resort to
the U-net-like decoder to concatenate or fuse encoder features. However, image
matting is essentially a pixel-wise regression, and the ideal situation is to
perceive the maximum opacity correspondence from the input image. In this
paper, we argue that the high-resolution feature representation, perception and
communication are more crucial for matting accuracy. Therefore, we propose an
Intensive Integration and Global Foreground Perception network (I2GFP) to
integrate wider and higher feature streams. Wider means we combine intensive
features in each decoder stage, while higher suggests we retain high-resolution
intermediate features and perceive large-scale foreground appearance. Our
motivation sacrifices model depth for a significant performance promotion. We
perform extensive experiments to prove the proposed I2GFP model, and
state-of-the-art results can be achieved on different public datasets.
