Foreground-aware Semantic Representations for Image Harmonization

Abstract

Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of a foreground to make it compatible with a background. Previous approaches to harmonize composites are based on training of encoder-decoder networks from scratch, which makes it challenging for a neural network to learn a high-level representation of objects. We propose a novel architecture to utilize the space of high-level features learned by a pre-trained classification network. We create our models as a combination of existing encoder-decoder architectures and a pretrained foreground-aware deep high-resolution network. We extensively evaluate the proposed method on the existing image harmonization benchmark and set up a new state-of-the-art in terms of MSE and PSNR metrics. The code and trained models are available publicly.