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DesnowNet: Context-Aware Deep Network for Snow Removal.

Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang

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    Summary
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    This study introduces DesnowNet, a novel deep learning network for removing snow from images. DesnowNet effectively handles diverse snow particle attributes, outperforming existing methods in computer vision and graphics applications.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Existing atmospheric particle removal methods rely on assumptions unsuitable for complex snow characteristics.
    • Snow removal is challenging due to varying particle size, shape, translucency, and chromatic aberration within images.
    • Current snow removal techniques often use hand-crafted features, limiting generalization.

    Purpose of the Study:

    • To develop an advanced deep learning network for effective snow particle removal from images.
    • To address the limitations of existing methods by considering diverse snow attributes.
    • To improve the generalization capability of snow removal algorithms.

    Main Methods:

    • Designed a multistage network, DesnowNet, to handle both translucent and opaque snow particles.
    • Differentiated snow attributes like translucency and chromatic aberration for precise estimation.
    • Employed a multi-scale network design to model the variability of snow particles.
    • Individually estimated residual complements to recover image details obscured by opaque snow.

    Main Results:

    • DesnowNet demonstrated superior performance compared to state-of-the-art methods on the Snow100K dataset.
    • Qualitative and quantitative experiments confirmed the effectiveness of the proposed approach.
    • The network successfully handled diverse snow particle sizes, shapes, and translucency levels.

    Conclusions:

    • The developed DesnowNet network offers a significant advancement in snow removal technology.
    • The approach shows promise for enhancing computer vision and graphics applications requiring clear imagery.
    • This method provides a robust solution for atmospheric phenomena removal, particularly snow.