MRCAN: Multi-scale Region Correlation-driven Adaptive Normalization for Image Harmonization
Luwen Duan, Min Wu, Haozhe Lou, Jun Yin, Xi Li · 2024
Image composition serves as a vital data augmentation technique commonly utilized for intelligent model training. In order to facilitate the optimization of image composition and improve the authenticity and efficacy of the composite images, this paper delves into the composite image harmonization technique to adjust the appearance of the foreground to be harmonious with the background. Current methods usually overlook the interrelation between the foreground and background content, typically transferring the style directly from the whole background to the foreground. In addition, conventional normalization methods were prone to a degradation in image quality of the foreground region after harmonization. To address these issues, we propose an innovative Adaptive Normalization method, which modulates the mean and standard deviation of foreground region specifically and pointedly, to harmonize the appearance while simultaneously preserving the texture structures of original foreground by considering the local information from the deviation maps. Moreover, we incorporate a Multi-scale Region Correlation-driven strategy to explore the correlation between the foreground and the background content, enabling the foreground object to fit the background semantics more seamlessly. Both ablation and comparison experiments on the iHarmony4 dataset demonstrate the effectiveness of our proposed method as well as the superiority of our model over other state-of-the-art image harmonization methods.