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Frequency domain decomposition network for optical remote sensing image destriping.

Yu Shi, Feiyan Wu, Yaozong Zhang

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    Summary
    This summary is machine-generated.

    This study introduces a novel network for optical remote sensing image destriping, effectively removing stripe noise by integrating spatial and frequency domain analysis. The method enhances target detection and recognition by preserving image details.

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    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Optical remote sensing images suffer from stripe noise due to imaging limitations.
    • This noise degrades performance in subsequent tasks like target detection and recognition.
    • Frequency domain analysis offers advantages for feature extraction compared to spatial domain methods.

    Purpose of the Study:

    • To develop an effective optical remote sensing image destriping network.
    • To leverage both spatial and frequency domain features for improved noise removal.
    • To enhance the accuracy of target detection and recognition by preserving image details.

    Main Methods:

    • A frequency domain decomposition-based network is proposed.
    • Wavelet decomposition and singular value decomposition are employed for feature extraction.
    • Spatial-frequency coupling and multi-scale adaptive fusion blocks are utilized to enhance feature transmission.

    Main Results:

    • The proposed method effectively distinguishes stripe and background features.
    • Stripe information is accurately extracted from high-frequency components.
    • The network successfully exploits the interaction between spatial and frequency domains.

    Conclusions:

    • The developed destriping network outperforms existing state-of-the-art methods.
    • The method demonstrates superior performance in both simulated and real-world experiments.
    • It achieves excellent stripe noise removal while preserving crucial image details.