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    This study introduces a novel variational method for structure + texture image decomposition, enhancing digital image processing. The approach effectively separates image components using curvature-guided filtering and learned variable exponent energy for texture suppression.

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    Area of Science:

    • Digital Image Processing
    • Computer Vision
    • Applied Mathematics

    Background:

    • Structure + texture image decomposition is crucial for various digital image processing tasks.
    • Existing methods require distinct strategies for processing structural and textural image components.

    Purpose of the Study:

    • To develop a new variational method for structure + texture image decomposition.
    • To improve the separation of structural and textural components in digital images.

    Main Methods:

    • Utilizing low-pass filtered level-set curvature of the input image as a guidance image.
    • Employing texture suppression via minimization of a variable exponent energy.
    • Learning the variable exponent from curvature-guided image filtering results.

    Main Results:

    • The proposed method achieves competitive performance compared to current state-of-the-art techniques in structure + texture image decomposition.
    • Demonstrated effectiveness through numerical experiments and consideration of several applications.

    Conclusions:

    • The developed variational approach offers an effective solution for structure + texture image decomposition.
    • The method shows promise for various digital image processing applications requiring component separation.