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Related Experiment Video

Updated: Jan 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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PH-Mamba: Enhancing Mamba With Position Encoding and Harmonized Attention for Image Deraining and Beyond.

Kui Jiang, Junjun Jiang, Xianming Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 12, 2025
    PubMed
    Summary
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    PH-Mamba enhances image deraining by integrating position encoding and harmonized attention. This novel framework improves Mamba

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Mamba models excel at long-range dependencies with linear complexity.
    • Mamba's 1D sequential processing hinders spatial dependency recovery in vision tasks, causing inefficiency.

    Purpose of the Study:

    • To introduce PH-Mamba, a framework that improves Mamba for image deraining by optimizing spatial dependency modeling.
    • To enhance efficiency and accuracy in vision tasks by addressing Mamba's limitations.

    Main Methods:

    • PH-Mamba integrates position encoding and harmonized attention for image deraining.
    • A position-guided hybrid Mamba module (PHMM) encodes spatial coordinates and perturbation features.
    • A harmonized Transformer within PHMM focuses on uncertain regions and suppresses noise.

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    991

    Main Results:

    • PH-Mamba achieves superior performance in low-level image restoration benchmarks.
    • Compared to NeRD, PH-Mamba shows a 0.60 dB PSNR improvement.
    • PH-Mamba significantly reduces parameters (88.9%), computation (36.2%), and inference time (63.0%).

    Conclusions:

    • PH-Mamba offers a more efficient and accurate approach to image deraining and other vision tasks.
    • The framework's combination of global positional guidance and local features strengthens contextual learning.
    • PH-Mamba represents a significant advancement over existing Mamba-based methods for vision applications.