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    Area of Science:

    • Image Processing
    • Computational Imaging
    • Computer Vision

    Background:

    • Quality-guided phase unwrapping (QGPU) is crucial for reconstructing phase information in various imaging applications.
    • The adjoin list is a critical component in QGPU algorithms.
    • The Indexed Interwoven Linked List (I2L2) is a data structure used for implementing adjoin lists in QGPU.

    Purpose of the Study:

    • To propose and evaluate three novel improvements to the I2L2 data structure.
    • To enhance the efficiency and performance of QGPU algorithms.
    • To reduce computational redundancy and mitigate the impact of quality value distribution.

    Main Methods:

    • Introduced resumed searching to track the highest non-empty level, reducing redundant computations.
    • Implemented an adaptive mapping between quality values and I2L2 levels to handle concentrated quality distributions.
    • Developed I2L2-H, a hybrid variant combining I2L2 and heap data structures.

    Main Results:

    • The improved I2L2 demonstrates a speedup of over 6 times compared to the original I2L2 in optimal scenarios.
    • The enhanced data structure enables processing of large phase maps in near real-time.
    • The proposed improvements effectively address computational redundancy and quality value distribution challenges.

    Conclusions:

    • The three proposed improvements significantly enhance the performance of the I2L2 data structure for QGPU.
    • The optimized I2L2 provides a more efficient solution for phase unwrapping, particularly for large datasets.
    • This work contributes to faster and more robust phase map processing in scientific imaging.