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arXiv · 2511.12785

Lightweight Optimal-Transport Harmonization on Edge Devices

Maria Larchenko·Dmitry Guskov·Alexander Lobashev·Georgy Derevyanko
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Abstract

Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem naturally arises in augmented reality (AR), yet harmonization algorithms are not currently integrated into AR pipelines because real-time solutions are scarce. In this work, we address color harmonization for AR by proposing a lightweight approach that supports on-device inference. For this, we leverage classical optimal transport theory by training a compact encoder to predict the Monge-Kantorovich transport map. We benchmark our MKL-Harmonizer algorithm against state-of-the-art methods and demonstrate that for real composite AR images our method achieves the best aggregated score. We release our dedicated AR dataset of composite images with pixel-accurate masks and data-gathering toolkit to support further data acquisition by researchers.

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Maria Larchenko, Dmitry Guskov, Alexander Lobashev, Georgy Derevyanko. 2025-11-16. Lightweight Optimal-Transport Harmonization on Edge Devices. https://arxiv.org/abs/2511.12785

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