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新理论探索用于成像散斑噪声的深度学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种基于极大极小理论的似然性深度学习方法，以解决合成孔径雷达和光学相干断层扫描等成像模态中的散斑噪声问题。该新框架能够处理乘性散斑噪声和加性高斯噪声，并兼容低维和稀疏高维特征。研究建立了有限样本上界，并推导了极大极小下界，表明与仅含加性高斯噪声的模型相比，估计的内在难度基本未变。 AI

影响 为将深度学习应用于科学成像中具有挑战性的噪声模型提供了理论基础。

排序理由 该集群包含一篇学术论文，详细介绍了在特定统计背景下用于深度学习的新理论框架。

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新理论探索用于成像散斑噪声的深度学习

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该集群包含一篇学术论文，详细介绍了在特定统计背景下用于深度学习的新理论框架。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Soham Jana ·

    基于似然的深度学习在斑点回归中的Minimax理论

    arXiv:2607.14064v1 Announce Type: cross Abstract: Speckle noise is a multiplicative noise commonly encountered in coherent imaging modalities such as synthetic aperture radar, optical coherence tomography, and digital holography. Although deep learning methods, in practice, have …

  2. arXiv stat.ML TIER_1 English(EN) · Soham Jana ·

    Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression

    Speckle noise is a multiplicative noise commonly encountered in coherent imaging modalities such as synthetic aperture radar, optical coherence tomography, and digital holography. Although deep learning methods, in practice, have achieved state-of-the-art performance for speckle …

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