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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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CAN: Cascade Augmentations Against Noise for Image Restoration.

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    This study introduces a Cascade Augmentation strategy against Noise (CAN) to improve image restoration models. CAN enhances robustness against various noise types, boosting generalization for diverse image restoration tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Current image restoration models lack generalization, failing when encountering unseen corruptions like noise.
    • Noise is a common artifact in real-world images, degrading restoration performance.

    Purpose of the Study:

    • To enhance the robustness and generalization ability of image restoration networks against various noise corruptions.
    • To develop a model-agnostic strategy that can be integrated into existing image restoration frameworks.

    Main Methods:

    • Proposed a novel Cascade Augmentation strategy against Noise (CAN).
    • CAN employs noise-aware augmentation (introducing diverse noise) and model-aware augmentation (using model randomness for spatial/frequency clues).
    • Developed noise corruption benchmark datasets for evaluation.

    Main Results:

    • The proposed CAN strategy significantly improves the robustness of image restoration frameworks against diverse noise.
    • Demonstrated strong generalization capability across various image restoration tasks.
    • Extensive evaluations confirmed the effectiveness of the augmentation approach.

    Conclusions:

    • The Cascade Augmentation strategy against Noise (CAN) effectively enhances image restoration model robustness.
    • CAN is a versatile, model-agnostic technique applicable to various image restoration architectures.
    • The developed benchmark datasets aid in evaluating and improving restoration network resilience to noise.