Efficient Image Harmonization via RGB Transformation

Image harmonization aims to adjust the appearance of the foreground to make it harmonious with the background, thereby maintaining visual consistency in composite images. Previous deep learning-based methods have mainly focused on reconstructing harmonized images with the same size as the input composite images, often leading to complex network structures and a large number of parameters. In this paper, we propose a simple yet effective lightweight image harmonization network architecture. First, we generate a low-resolution 3-channel feature map to represent the rough variations in the RGB channels of a composite image, which is then upsampled and added to this composite image to obtain a preliminary harmonization result. Then, a refinement module is applied to refine the preliminary result and output the final harmonized image. Additionally, we design a dynamic data generation and training strategy to pre-train our model on another dataset. Experimental results on the iHarmony4 dataset show that our method indicates a significant reduction in the number of parameters compared to other methods, yet it still achieved competitive performance.