Image harmonization is an essential step in image composition that adjuststhe appearance of composite foreground to address the inconsistency betweenforeground and background. Existing methods primarily operate in correlated$RGB$RGB color space, leading to entangled features and limited representationability. In contrast, decorrelated color space (e.g., $Lab$Lab) has decorrelatedchannels that provide disentangled color and illumination statistics. In thispaper, we explore image harmonization in dual color spaces, which supplementsentangled $RGB$RGB features with disentangled $L$L, $a$a, $b$b features to alleviatethe workload in harmonization process. The network comprises a $RGB$RGBharmonization backbone, an $Lab$Lab encoding module, and an $Lab$Lab control module.The backbone is a U-Net network translating composite image to harmonizedimage. Three encoders in $Lab$Lab encoding module extract three control codesindependently from $L$L, $a$a, $b$b channels, which are used to manipulate thedecoder features in harmonization backbone via $Lab$Lab control module. Our codeand model are available at\href{https://github.com/bcmi/DucoNet-Image-Harmonization}{https://github.com/bcmi/DucoNet-Image-Harmonization}.