  In natural image matting, the goal is to estimate the opacity of the
foreground object in the image. This opacity controls the way the foreground
and background is blended in transparent regions. In recent years, advances in
deep learning have led to many natural image matting algorithms that have
achieved outstanding performance in a fully automatic manner. However, most of
these algorithms only predict the alpha matte from the image, which is not
sufficient to create high-quality compositions. Further, it is not possible to
manually interact with these algorithms in any way except by directly changing
their input or output. We propose a novel recurrent neural network that can be
used as a post-processing method to recover the foreground and background
colors of an image, given an initial alpha estimation. Our method outperforms
the state-of-the-art in color estimation for natural image matting and show
that the recurrent nature of our method allows users to easily change candidate
solutions that lead to superior color estimations.
