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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Learning Diverse Tone Styles for Image Retouching.

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    This study introduces a novel normalizing flow-based method for diverse image retouching, addressing limitations of deterministic models. The approach effectively disentangles style from content, generating varied retouching results to meet user aesthetic preferences.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image retouching is subjective, with diverse user aesthetic preferences.
    • Existing deterministic models struggle to capture this diversity and expert variability.

    Purpose of the Study:

    • To develop a flexible image retouching method capable of generating diverse results.
    • To address limitations of deterministic models in capturing subjective aesthetic preferences.

    Main Methods:

    • Proposed a normalizing flow-based architecture for diverse image retouching.
    • Introduced a joint-training pipeline with a style encoder, conditional RetouchNet, and image tone style normalizing flow (TSFlow).
    • Learned retouching styles in a one-dimensional style space to disentangle style from content and avoid spatial disharmony.

    Main Results:

    • The TSFlow module effectively maps style representations into a Gaussian distribution.
    • Generated diverse image tone style vectors by sampling from the learned distribution.
    • Achieved favorable performance against state-of-the-art methods on MIT-Adobe FiveK and PPR10K datasets.

    Conclusions:

    • The proposed method effectively generates diverse image retouching results.
    • Successfully satisfies varied human aesthetic preferences.
    • Demonstrates the advantage of learning in a one-dimensional style space for image retouching.