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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Stereo image restoration in adverse conditions (low-light, rain, low resolution) is challenging.
    • Frequency decomposition is effective for monocular restoration (high-frequency for details, low-frequency for noise/illumination).
    • Existing stereo methods lack cross-view frequency decomposition for enhanced restoration.

    Purpose of the Study:

    • To propose a novel frequency-aware framework for stereo image restoration.
    • To leverage cross-view complementary information through frequency decomposition.
    • To improve restoration quality in adverse environments.

    Main Methods:

    • A frequency-aware framework with Frequency Decomposition Module (FDM), Detail Interaction Module (DIM), Structural Interaction Module (SIM), and Adaptive Fusion Module (AFM).
    • FDM uses learnable filters for image decomposition into high- and low-frequency components.
    • DIM uses deformable convolution for high-frequency detail enhancement; SIM uses cross-view row-wise attention for low-frequency structural correlation.
    • AFM adaptively fuses frequency-specific information.

    Main Results:

    • The proposed framework achieves state-of-the-art performance in low-light enhancement and rain removal.
    • It shows highly competitive results in stereo super-resolution.
    • Demonstrates efficacy and generalizability across diverse stereo restoration tasks.

    Conclusions:

    • The frequency-aware framework effectively exploits cross-view complementary information via frequency decomposition.
    • It significantly enhances stereo image restoration quality in challenging conditions.
    • The method offers a promising direction for advanced stereo image restoration.