Shadow removal is an essential task for scene understanding. Many studies
consider only matching the image contents, which often causes two types of
ghosts: color in-consistencies in shadow regions or artifacts on shadow
boundaries. In this paper, we tackle these issues in two ways. First, to
carefully learn the border artifacts-free image, we propose a novel network
structure named the dual hierarchically aggregation network~(DHAN). It contains
a series of growth dilated convolutions as the backbone without any
down-samplings, and we hierarchically aggregate multi-context features for
attention and prediction, respectively. Second, we argue that training on a
limited dataset restricts the textural understanding of the network, which
leads to the shadow region color in-consistencies. Currently, the largest
dataset contains 2k+ shadow/shadow-free image pairs. However, it has only 0.1k+
unique scenes since many samples share exactly the same background with
different shadow positions. Thus, we design a shadow matting generative
adversarial network~(SMGAN) to synthesize realistic shadow mattings from a
given shadow mask and shadow-free image. With the help of novel masks or
scenes, we enhance the current datasets using synthesized shadow images.
Experiments show that our DHAN can erase the shadows and produce high-quality
ghost-free images. After training on the synthesized and real datasets, our
network outperforms other state-of-the-art methods by a large margin. The code
is available: this http URL

本文介绍了通过使用名为 DHAN 和 SMGAN 的网络结构来解决阴影区域颜色不一致和边界失真的问题，以提高场景理解的能力。