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Bayer Demosaicking With Polynomial Interpolation.

Jiaji Wu, Marco Anisetti, Wei Wu

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    This study introduces a novel polynomial interpolation-based demosaicking algorithm. The new method enhances image quality and reduces artifacts, outperforming existing techniques.

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    Area of Science:

    • Digital Image Processing
    • Computer Vision

    Background:

    • Demosaicking reconstructs full-color images from sensor data.
    • It is essential for devices with cameras, like smartphones and tablets.

    Purpose of the Study:

    • To introduce a new demosaicking algorithm based on polynomial interpolation.
    • To improve image quality and reduce artifacts in digital images.

    Main Methods:

    • Developed novel error predictors using polynomial interpolation.
    • Implemented an edge classification strategy based on color differences.
    • Applied a weighted sum strategy for a refinement stage to enhance image quality.

    Main Results:

    • The proposed predictors offer a viable alternative to bilinear or Laplacian interpolation.
    • Experimental results demonstrate substantial improvements in objective metrics (CPSNR, S-CIELAB ΔE*, FSIM).
    • The method shows superior visual performance compared to existing demosaicking techniques.

    Conclusions:

    • The novel polynomial interpolation-based demosaicking algorithm significantly enhances image reconstruction.
    • The method effectively reduces demosaicking artifacts and improves overall image quality.