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英文字典中文字典相关资料:


  • PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One . . .
    Addressing limitations of existing all-in-one restoration methods that overly rely on implicit feature representations and lack explicit modeling of degradation physics, this paper proposes Physics-Guided Degradation-Adaptive Experts (PhyDAE)
  • GitHub - HIT-SIRS PhyDAE
    Unlike prior "black-box" all-in-one models, PhyDAE bridges physical imaging principles with learnable expert mechanisms, achieving interpretable, efficient, and physically consistent restoration
  • arxiv每日论文-计算机视觉2025-10-13 - 知乎
    这些复杂且异质的退化情况对图像质量和下游解译任务构成了严峻挑战。 针对现有一体化恢复方法过度依赖隐式特征表示且缺乏对退化物理过程的显式建模这一局限性，本文提出了物理引导的退化自适应专家模型（PhyDAE）。
  • PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One . . .
    Addressing limitations of existing all-in-one restoration methods that overly rely on implicit feature representations and lack explicit modeling of degradation physics, this article proposes
  • IEEE Transactions on Geoscience and Remote Sensing
    Dong, Zhe; Zhang, Zhengning; Sun, Yuzhe; Jiang, Haochen; Liu, Tianzhu; Gu, Yanfeng (2026) PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration
  • PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One . . .
    Addressing limitations of existing all-in-one restoration methods that overly rely on implicit feature representations and lack explicit modeling of degradation physics, this article proposes physics-guided degradation-adaptive experts (PhyDAE)
  • PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One . . .
    PhyDAE incorporates a comprehensive probabilistic expert allocator, which leverages multiple complementary information sources to achieve more precise and efficient expert selection
  • PhyDAE: Physics-Guided Degradation-Adaptive Experts for . . .
    Article "PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")
  • Abstract arXiv:2510. 08653v1 [cs. CV] 9 Oct 2025
    Notably, PhyDAE substantially improves restoration quality while achieving significant reductions in parameter count and computational complexity, resulting in remarkable efficiency gains com- paredtomainstreamapproachesandachievingoptimalbalancebetweenperformanceandefficiency
  • Release PhyDAE models and datasets on Hugging Face
    Currently, the PhyDAE model checkpoints, as well as the MD-RSID, MD-RRSHID, and MDRS-Landsat datasets, have been uploaded to Baidu Netdisk We’ll consider adding them to the 🤗 Hub in the future to further improve accessibility and discoverability





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