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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Pixelwise noise is handled by regression methods, but structural noise remains a challenge.
    • Existing methods struggle with structural noise and iterative computations, leading to high time consumption.

    Purpose of the Study:

    • To develop a novel method for reconstructing images with structural noise for improved classification.
    • To overcome the limitations of existing regression-based methods in handling structural noise and computational efficiency.

    Main Methods:

    • Proposed a low-rank latent pattern approximation (LLPA) model to reconstruct images with structural noise.
    • Utilized rank function for residual structure characterization and Frobenius norm for error constraint.
    • Developed a closed-form solution using singular value thresholding and extended LLPA to matrix regression.

    Main Results:

    • Theoretic analysis confirms LLPA effectively removes structural noise during classification.
    • Extended LLPA efficiently solved using alternating direction of multipliers and Gaussian back substitution.
    • Experimental results show superior robustness against occlusion and illumination changes.

    Conclusions:

    • LLPA provides a robust and efficient solution for image classification with structural noise.
    • The method outperforms state-of-the-art reconstruction-based and deep neural network methods.