Learning multi-color curve for image harmonization

Due to the varying shooting conditions, composite images often lack realism between the foreground and the back ground. As an important and challenging visual task, image harmonization can effectively improve visual effect of composite images. Currently, image harmonization methods have achieved satisfied performance on public dataset. However, in some challenging examples with substantial color disparities between the foreground and the background, existing methods get poor results. To solve this problem, we propose a Multi-color Curve Net that processes images through multiple color spaces to capture richer color information. Our Multi-color Curve Net performs multi-stage curve learning in different color spaces with the encoder composed of modified Transformer blocks. Simultaneously, we introduce a Multi-color Integration Module to effectively fuse the information extracted from different color spaces and further improve the results by a lightweight Fine-grained Optimization Module. The Multi-color Curve Net gains high performance while maintaining a small parameter scale. Experiments on benchmark demonstrate that the Multi-color Curve Net outperforms state-of-the-art methods in terms of peak signal to-noise ratio (PSNR), structural similarity (SSIM) and foreground mean squared error (fMSE) with fewer parameters. The code for our method is available at https://github.com/gmrj2024/MC2Net.