Learning Diverse Tone Styles for Image Retouching
Haolin Wang, Jiawei Zhang, Ming Liu, Xiaohe Wu, Wangmeng Zuo

TL;DR
This paper introduces a novel style-disentangled approach using normalizing flows for diverse image retouching, enabling flexible and subjective aesthetic preferences with stable style representations.
Contribution
It proposes a style domain learning framework with a style encoder, RetouchNet, and TSFlow for disentangling and generating diverse retouching styles, improving over deterministic models.
Findings
Outperforms state-of-the-art methods on MIT-Adobe FiveK and PPR10K datasets.
Effectively generates diverse retouching results matching human aesthetic preferences.
Disentangles style from content for stable and flexible image retouching.
Abstract
Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic diversity of an expert due to the targeted processing on different images is also deficiently described. To circumvent such issues, we propose to learn diverse image retouching with normalizing flow-based architectures. Unlike current flow-based methods which directly generate the output image, we argue that learning in a style domain could (i) disentangle the retouching styles from the image content, (ii) lead to a stable style presentation form, and (iii) avoid the spatial disharmony effects. For obtaining meaningful image tone…
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Taxonomy
TopicsImage Enhancement Techniques · Visual Attention and Saliency Detection · Image Retrieval and Classification Techniques
