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Related Concept Videos

Atomic Absorption Spectroscopy: Interference01:25

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Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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[Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis].

Jia Wen, Jun-suo Zhao, Cai-ling Wang

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |May 28, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Interference hyperspectral images contain noise. Morphological Component Analysis (MCA) effectively separates interference stripes and background signals. An improved MCA algorithm accelerates processing while maintaining high-quality image decomposition.

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    Area of Science:

    • Image processing
    • Signal processing
    • Hyperspectral imaging

    Context:

    • Interference hyperspectral image data is characterized by vertical interference stripes and horizontal displacements.
    • These artifacts disrupt the data's structure, limiting the effectiveness of traditional compression and compressive sensing algorithms.
    • The distinct characteristics of interference stripes and background signals necessitate different sparse representation bases.

    Purpose:

    • To separate interference stripes and background signals in hyperspectral images using Morphological Component Analysis (MCA).
    • To develop an improved MCA algorithm that enhances computational efficiency and iterative convergence speed for hyperspectral image data.
    • To introduce an adaptive threshold update mode to accelerate the MCA algorithm's processing.

    Summary:

    • Morphological Component Analysis (MCA) is employed to decompose hyperspectral images by separating interference stripes from background signals based on their differing sparse representations.
    • An enhanced MCA algorithm is proposed, featuring improved iterative convergence conditions and an adaptive threshold update strategy to accelerate computation.
    • The improved algorithm maintains the high-quality decomposition of the traditional MCA while significantly reducing iteration times and improving efficiency.

    Impact:

    • The study demonstrates the effectiveness of MCA in perfectly decomposing interference hyperspectral images.
    • The improved MCA algorithm offers a faster and more efficient solution for hyperspectral image processing, particularly for large datasets.
    • This work provides a valuable approach for applying compressive sensing theories to interference-rich hyperspectral imaging.