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新的DPSF-Net采用双先验空频方法增强遥感图像去雾效果

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DPSF-Net，一种新颖的深度学习网络，旨在通过去除雾霾来提高真实世界遥感图像的质量。该网络独特地结合了空间域和频域特征学习，利用了雾霾RGB图像和暗通道先验作为输入，以更好地区分大气雾霾和地表细节。实验表明，DPSF-Net在RRSHID基准测试中优于现有方法，并在恢复质量、参数数量和计算复杂度方面取得了良好的平衡。 AI

影响 这一新模型有望提高遥感数据在各种应用中的清晰度和实用性。

排序理由 详细介绍图像处理新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的DPSF-Net采用双先验空频方法增强遥感图像去雾效果

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详细介绍图像处理新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mei Lu, Shangliang Shao, Shanliang Yao ·

    DPSF-Net：用于真实世界遥感图像去雾的双先验空频网络

    arXiv:2609.06962v1 Announce Type: cross Abstract: Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods r…

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