bcmi/Awesome-Image-Harmonization

What it solves

Researchers and developers working on image compositing often face the problem of a inserted foreground object looking out of place because its color, lighting, or style does not match the background. This project gathers the key resources for the specific task of image harmonization, which aims to adjust the foreground so it blends naturally with the background.

How it works

The project is an “awesome list” – a manually curated collection of links and summaries. It organizes resources into sections: a survey paper for background reading, an online demo built on the libcom toolbox, a leaderboard linking to state-of-the-art results, a summary of color transfer strategies, and a large list of research papers. The papers are grouped by whether they use supervised or unsupervised deep learning methods, and each entry links to the paper and often to code or datasets. It also provides a separate list of datasets and other useful resources.

Who it’s for

This is aimed at researchers, graduate students, and engineers who want to understand or work on image harmonization. It is also a starting point for anyone exploring image composition or object insertion, since it links to related topic lists.

Highlights

• Curated survey article and online demo for hands-on exploration
• Leaderboard link showing current state-of-the-art performance on the iHarmony4 benchmark
• Summary of color transfer techniques specifically applicable to harmonization
• Cross-referenced papers with code and dataset links for many entries
• Categorization into supervised and unsupervised methods for easier literature review