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Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration.

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    This study introduces an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) model to improve High Dynamic Range (HDR) imaging. The novel framework effectively handles saturation and motion artifacts for clearer, artifact-free HDR images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • High Dynamic Range (HDR) imaging is crucial for modern platforms.
    • Multi-exposure fusion faces challenges like saturation, motion, ghosting, noise, and blur.

    Purpose of the Study:

    • To propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) model for HDR restoration.
    • To address content association ambiguities and artifacts in multi-exposure fusion within a unified framework.

    Main Methods:

    • An efficient two-stream structure for texture feature transfer and multi-exposure fusion.
    • A neural feature transfer mechanism using multi-scale VGG features for spatial correspondence.
    • Novel attention mechanisms: motion, saturation, and scale attention modules.
    • A progressive texture blending module for multi-scale feature fusion.

    Main Results:

    • The APNT-Fusion model effectively suppresses ghosting, noise, and blur.
    • Attention modules successfully detect and suppress content discrepancies and misalignments.
    • The model demonstrates superior performance in qualitative and quantitative evaluations.

    Conclusions:

    • The proposed APNT-Fusion model offers a coherent framework for HDR restoration.
    • Novel attention mechanisms and progressive blending significantly enhance fusion quality.
    • The method outperforms existing state-of-the-art techniques in handling challenging fusion artifacts.