Retrieval Augmented Image Harmonization
Haolin Wang, Ming Liu, Zifei Yan, Chao Zhou, Longan Xiao, Wangmeng Zuo

TL;DR
The paper introduces Raiha, a retrieval-augmented framework for image harmonization that improves results by finding relevant reference images and better understanding image content, addressing issues of ill-posedness and irrelevant attention.
Contribution
It proposes a novel retrieval-augmented approach with an efficient retrieval method and content priors, enhancing image harmonization performance significantly.
Findings
Improved harmonization quality with retrieval-augmented method
Effective retrieval of reference images with similar objects and illumination
Enhanced performance over existing methods in both non-reference and retrieval-augmented settings
Abstract
When embedding objects (foreground) into images (background), considering the influence of photography conditions like illumination, it is usually necessary to perform image harmonization to make the foreground object coordinate with the background image in terms of brightness, color, and etc. Although existing image harmonization methods have made continuous efforts toward visually pleasing results, they are still plagued by two main issues. Firstly, the image harmonization becomes highly ill-posed when there are no contents similar to the foreground object in the background, making the harmonization results unreliable. Secondly, even when similar contents are available, the harmonization process is often interfered with by irrelevant areas, mainly attributed to an insufficient understanding of image contents and inaccurate attention. As a remedy, we present a retrieval-augmented image…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques
MethodsSoftmax · Attention Is All You Need
