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Comparison of interpolating methods for image resampling.

J Parker, R V Kenyon, D E Troxel

    IEEE Transactions on Medical Imaging
    |January 1, 1983
    PubMed
    Summary
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    Image resampling often degrades quality. High-resolution cubic spline functions significantly improve image quality during resampling compared to simpler methods like nearest neighbor or linear interpolation.

    Area of Science:

    • Digital Image Processing
    • Computer Vision
    • Signal Processing

    Background:

    • Image resampling to new coordinates, such as during rotation, frequently results in noticeable image quality degradation.
    • Preserving image fidelity during resampling necessitates the use of an ideal low-pass filter as the interpolating function.

    Purpose of the Study:

    • To evaluate and compare the performance of five different interpolating functions for image resampling.
    • To identify the optimal function for preserving image quality during resampling.

    Main Methods:

    • Five convolving functions were assessed: nearest neighbor, linear, cubic B-spline, high-resolution cubic spline with edge enhancement (a = -1), and high-resolution cubic spline (a = -0.5).
    • Frequency response was analyzed to determine interpolation effectiveness.

    Related Experiment Videos

  • The impact of resampled point alignment relative to the initial coordinate system was investigated.

    • Main Results:

    • Functions extending over four pixels (cubic B-spline variants) demonstrated superior frequency response compared to those over one or two pixels.
    • Nearest neighbor interpolation introduced pixel shifts up to 0.5 pixels.
    • Linear and cubic B-spline methods resulted in image smoothing.
    • High-resolution cubic spline functions yielded the best interpolation performance.

    Conclusions:

    • High-resolution cubic spline functions offer superior image quality preservation during resampling compared to nearest neighbor, linear, and cubic B-spline methods.
    • Optimal resampling performance is achieved using high-resolution cubic splines, despite a slight increase in computational cost.
    • The alignment of resampled points relative to the original coordinate system critically influences interpolation outcomes.