  Deep neural networks for automatic image colorization often suffer from the
color-bleeding artifact, a problematic color spreading near the boundaries
between adjacent objects. Such color-bleeding artifacts debase the reality of
generated outputs, limiting the applicability of colorization models in
practice. Although previous approaches have attempted to address this problem
in an automatic manner, they tend to work only in limited cases where a high
contrast of gray-scale values are given in an input image. Alternatively,
leveraging user interactions would be a promising approach for solving this
color-breeding artifacts. In this paper, we propose a novel edge-enhancing
network for the regions of interest via simple user scribbles indicating where
to enhance. In addition, our method requires a minimal amount of effort from
users for their satisfactory enhancement. Experimental results demonstrate that
our interactive edge-enhancing approach effectively improves the color-bleeding
artifacts compared to the existing baselines across various datasets.
