Deep Image Harmonization in Dual Color Spaces
Linfeng Tan Jiangtong Li Li Niu Liqing Zhang
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}.