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Related Experiment Video

Updated: Oct 28, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

349

Non-Local Representation Based Mutual Affine-Transfer Network for Photorealistic Stylization.

Ying Qu, Zhenzhou Shao, Hairong Qi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 14, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel non-local representation scheme for photorealistic stylization. The method achieves global color consistency in stylized images, enhancing realism without semantic matching.

    Related Experiment Videos

    Last Updated: Oct 28, 2025

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    349

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Photorealistic stylization aims to create natural, camera-like images.
    • Current methods struggle with global color consistency due to local stylization.

    Purpose of the Study:

    • To develop a method for photorealistic stylization with improved global color consistency.
    • To address the limitations of existing local stylization techniques.

    Main Methods:

    • Proposed a non-local representation scheme constrained with a mutual affine-transfer network (NL-MAT).
    • Utilized dictionary-based decomposition to decouple representations and color information.
    • Incorporated context correspondence for natural style transfer.

    Main Results:

    • Achieved local style transfer with global color consistency.
    • Preserved spatial structure and original color properties of the content image.
    • Demonstrated superior photorealistic results compared to state-of-the-art methods.

    Conclusions:

    • The NL-MAT method offers a novel approach to photorealistic stylization.
    • This is the first method to use non-local representations for this task without semantic matching.
    • The technique successfully enhances realism by maintaining global color consistency.