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Structure and Texture-Aware Image Decomposition via Training a Neural Network.

Fei Zhou, Qun Chen, Bozhi Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 4, 2020
    PubMed
    Summary
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    This study introduces novel structure-aware and texture-aware measures for image decomposition. Our deep learning approach enhances structure-texture separation, leading to sharper edges and improved applications like detail enhancement.

    Area of Science:

    • Computational graphics
    • Image processing

    Background:

    • Structure-texture image decomposition (STD) is a challenging fundamental problem.
    • Existing methods using edge strengths and spatial scales inadequately describe image structures and textures.

    Purpose of the Study:

    • To introduce novel structure-aware and texture-aware measures for improved image structure-texture decomposition.
    • To develop a unified deep learning framework for optimizing the STD cost function.

    Main Methods:

    • Proposed two complementary measures: one for local gradient anisotropy and another for signal pattern repeatability.
    • Employed a deep neural network architecture to optimize the STD cost function in a unified manner, avoiding traditional optimization challenges.

    Main Results:

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    • The proposed method achieves superior separation of image structure and texture compared to state-of-the-art methods.
    • Results show sharper edges in the structural component after decomposition.

    Conclusions:

    • The novel measures and deep learning approach significantly advance structure-texture image decomposition.
    • The method's effectiveness is demonstrated through successful applications in detail enhancement, edge detection, and super-resolution quality assessment.