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    We introduce the Degradation Relationship Index (DRI) to quantify image degradation relationships. Appropriate mixing of degradations, determined by our DPD method, enhances image restoration performance.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Image restoration research often overlooks the complex interplay between different image degradations.
    • Improving restoration performance requires understanding how combining degradations affects model training.

    Purpose of the Study:

    • To quantify the relationship between various image degradations.
    • To develop a method for improving image restoration performance by strategically combining degradations.

    Main Methods:

    • Propose the Degradation Relationship Index (DRI) based on validation loss differences between models trained with single vs. combined degradations.
    • Introduce the Degradation Proportion Determination (DPD) method to identify optimal degradation combinations for specific restoration tasks.

    Main Results:

    • A positive DRI indicates that incorporating auxiliary degradations improves performance on the anchor task.
    • The proportion of auxiliary degradation significantly impacts the anchor task's performance, requiring careful balancing.
    • The DPD method effectively predicts performance enhancements for various degradation combinations.

    Conclusions:

    • Quantifying degradation relationships is key to improving image restoration.
    • Strategic combination and proportioning of degradations, guided by DRI and DPD, enhance restoration effectiveness.
    • The proposed methods demonstrate generalizability across diverse image restoration tasks like noise, rain, haze, and snow removal.