  Image harmonization is an essential step in image composition that adjusts
the appearance of composite foreground to address the inconsistency between
foreground and background. Existing methods primarily operate in correlated
$RGB$ color space, leading to entangled features and limited representation
ability. In contrast, decorrelated color space (e.g., $Lab$) has decorrelated
channels that provide disentangled color and illumination statistics. In this
paper, we explore image harmonization in dual color spaces, which supplements
entangled $RGB$ features with disentangled $L$, $a$, $b$ features to alleviate
the workload in harmonization process. The network comprises a $RGB$
harmonization backbone, an $Lab$ encoding module, and an $Lab$ control module.
The backbone is a U-Net network translating composite image to harmonized
image. Three encoders in $Lab$ encoding module extract three control codes
independently from $L$, $a$, $b$ channels, which are used to manipulate the
decoder features in harmonization backbone via $Lab$ control module. Our code
and model are available at
\href{https://github.com/bcmi/DucoNet-Image-Harmonization}{https://github.com/bcmi/DucoNet-Image-Harmonization}.
