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English(EN) RPL-UIE: Reliable Prior Learning for Underwater Image Enhancement

新方法利用可靠先验学习增强水下图像

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RPL-UIE，一个新颖的两阶段框架，用于增强水下图像。该方法使用教师-学生模型从退化图像和参考图像中学习可靠的空间先验，学生模型在推理时无需参考图像即可利用这些先验进行恢复。该框架结合了残差先验细化扩散和频率感知先验残差校准来细化这些先验，从而提高图像质量并在目标检测和实例分割等下游任务中获得更好的性能。 AI

影响 这项研究可以改善水下环境的视觉感知，造福于海洋生物学和自主导航等应用。

排序理由 该集群包含一篇详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新方法利用可靠先验学习增强水下图像

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该集群包含一篇详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yifan Chen, Jiaming Liu, Ye Zheng, Zhe Sun, Tao Chen ·

    RPL-UIE：用于水下图像增强的可靠先验学习

    arXiv:2608.00137v1 Announce Type: cross Abstract: Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Although existing generative methods can handle compl…

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