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Updated: Feb 1, 2026

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应用 Self2Self 自主监督的消噪框架,用于空中马射线光谱数据
Fan Li1, Chao Xiong1, Jiahao He2
1National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, Nanchang, China; School of Nuclear Science and Engineering, East China University of Technology, Nanchang, China.
概括
Self2Self算法有效地消除了空中马射线光谱 (AGRS) 数据,改善了矿产勘探的图像质量. 这种自我监督的方法通过重建掩盖的像素来提高数据保真度,即使在低计数率下也能产生高质量的结果.
科学领域:
- 地质物理学 地质物理学
- 图像处理 图像处理
- 数据科学数据科学数据科学
背景情况:
- 空载玛射线光谱仪 (AGRS) 数据质量受到飞行变化,探测器极限和背景噪声的影响.
- 这些因素阻碍了高质量的现场采样,并限制了传统的无色化有效性.
研究的目的:
- 调查Self2Self对AGRS数据的自我监督拒绝算法的可行性.
- 评估算法的提高图像质量和保存光谱特征的能力.
主要方法:
- 采用Self2Self算法,使用一个单一的噪音图像和随机的伯努利面具.
- 利用增强的U-Net架构进行掩盖的像素重建.
- 集成的脱落和整体策略,用于减小差异和高保真输出.
主要成果:
- 实现的PSNR值为32.03 dB (Th),30.53 dB (K) 和28.66 dB (U).
- 获得的SSIM值为0.99 (Th),0.97 (K) 和0.91 (U).
- 证明FPSNR增强2.432.60dB,对的相对改善为29.6%,保留光谱峰值和梯度,同时减少工件.
结论:
- Self2Self算法有效地消除了AGRS数据,提高了图像质量和光谱特征的保存.
- 这种自我监督的方法对于计数率和噪声低的数据集来说是强大的.
- 该方法为AGRS在矿产勘探和环境监测中提供了新的可能性.
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