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High dynamic range image reconstruction based on efficient multi-scale attention.

GuoWei Xiang, BenDu Bai

    Applied Optics
    |August 12, 2025
    PubMed
    Summary
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    This study introduces an efficient hybrid transformer network for high dynamic range (HDR) image reconstruction, effectively reducing ghosting artifacts and improving computational speed for clearer images.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Ghosting artifacts from motion and complex transformers hinder high dynamic range (HDR) image reconstruction.
    • Existing methods struggle with computational complexity and artifact reduction.

    Purpose of the Study:

    • To develop an efficient HDR image reconstruction network.
    • To address ghosting artifacts and computational challenges in HDR imaging.

    Main Methods:

    • Proposed an HDR reconstruction network with a hybrid perception transformer.
    • Integrated efficient multi-scale attention mechanisms, including spatial attention for cross-exposure fusion.
    • Utilized an efficient context extractor and window attention for enhanced global-local feature interaction.

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    Main Results:

    • The network effectively preserves texture features from images with varying exposures.
    • Successfully reduced information interference in over- and under-saturated regions.
    • Achieved favorable reconstruction performance with significantly improved computational efficiency.

    Conclusions:

    • The proposed hybrid transformer network offers an effective solution for HDR image reconstruction.
    • Demonstrates superior performance in artifact reduction and computational efficiency compared to traditional methods.