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Restore-RWKV: Efficient and Effective Medical Image Restoration With RWKV.

Zhiwen Yang, Jiayin Li, Hui Zhang

    IEEE Journal of Biomedical and Health Informatics
    |July 15, 2025
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    Summary
    This summary is machine-generated.

    Restore-RWKV, a novel model, enhances medical image restoration by adapting the Receptance Weighted Key Value (RWKV) architecture for 2D data. It achieves state-of-the-art results efficiently, even with a lightweight design.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Transformers offer powerful medical image restoration but face limitations due to quadratic complexity with high-resolution images.
    • The Receptance Weighted Key Value (RWKV) model shows promise for efficient long-sequence processing in natural language processing.

    Purpose of the Study:

    • To introduce Restore-RWKV, the first RWKV-based model tailored for medical image restoration.
    • To address the limitations of existing models in handling high-resolution medical imaging data.

    Main Methods:

    • Developed a recurrent WKV (Re-WKV) attention mechanism for linear-complexity global dependency modeling in 2D images.
    • Introduced an omnidirectional token shift (Omni-Shift) layer to improve local dependency capture.
    • Adapted the 1D RWKV model for 2D spatial data processing.

    Main Results:

    • Restore-RWKV achieves comparable or superior performance to state-of-the-art methods, even with a lightweight variant (1.16M parameters).
    • Demonstrated effectiveness across diverse tasks: PET synthesis, CT denoising, MRI super-resolution, and all-in-one restoration.
    • Achieved state-of-the-art performance in medical image restoration tasks.

    Conclusions:

    • Restore-RWKV offers an efficient and effective solution for medical image restoration challenges.
    • The proposed Re-WKV and Omni-Shift modifications successfully adapt RWKV for 2D medical imaging.
    • This model presents a promising advancement for high-resolution medical image processing.