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    Area of Science:

    • Image Processing
    • Computer Vision
    • Signal Processing

    Background:

    • Multispectral image (MSI) denoising is challenging due to efficiency and effectiveness issues.
    • Existing nonlocal and transform-domain methods leverage group-level correlations for sparsity, often using recursive strategies with many similar patches.
    • The significance of patch-level representation in MSI denoising has been underestimated.

    Purpose of the Study:

    • To investigate the influence and potential of patch-level representation in MSI denoising.
    • To develop a novel denoising method based on patch-level analysis.
    • To improve the efficiency and effectiveness of MSI denoising techniques.

    Main Methods:

    • A general formulation with a block diagonal matrix was considered to analyze patch-level representation.
    • A proper global patch basis was trained.
    • A local principal component analysis (PCA) transform was applied in the grouping dimension.
    • A transform-threshold-inverse method was implemented for denoising.
    • A fast implementation strategy was developed to reduce computational complexity.

    Main Results:

    • The proposed patch-level representation method produced very competitive denoising results.
    • The approach demonstrated robustness across simulated and real MSI datasets.
    • The method proved to be effective and efficient in practice.
    • Experiments confirmed the advantages of focusing on patch-level correlations over group-level ones.

    Conclusions:

    • Patch-level representation is crucial for effective and efficient MSI denoising.
    • The developed method offers a robust and computationally efficient solution for MSI denoising.
    • This work provides a new perspective on utilizing self-similarity and sparse representation for image denoising applications.