Papers › Deep Image Harmonization in Dual Color Spaces
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}.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Harmonization | HAdobe5k(1024$\times$1024) | DucoNet | MSE | 10.94 | #1 of 7 | Archive leaderboard | report |
| Image Harmonization | HAdobe5k(1024$\times$1024) | DucoNet | PSNR | 41.37 | #1 of 7 | Archive leaderboard | report |
| Image Harmonization | HAdobe5k(1024$\times$1024) | DucoNet | SSIM | 0.9886 | #1 of 7 | Archive leaderboard | report |
| Image Harmonization | HAdobe5k(1024$\times$1024) | DucoNet | fMSE | 80.69 | #1 of 7 | Archive leaderboard | report |
| Image Harmonization | iHarmony4 | DucoNet | MSE | 18.47 | #3 of 16 | Archive leaderboard | report |
| Image Harmonization | iHarmony4 | DucoNet | PSNR | 39.17 | #3 of 16 | Archive leaderboard | report |
| Image Harmonization | iHarmony4 | DucoNet | fMSE | 212.53 | #3 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections