在超光谱成像中自我监督消除非独立噪声
Guangrui Ding1,2, Chang Liu3,2, Jiaze Yin1,2
1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA, 02215.
Newton ((New York, N.Y.)
|August 15, 2025
概括
一种新的深度学习方法,自主监督的PErmutation Noise2noise Denoising (SPEND),有效地从单个超谱图像中去除复杂的噪声. 这种技术显著改善了生物样本中分子识别的信号噪声比.
科学领域:
- 频谱学是一种光谱学.
- 显微镜的使用方法
- 生物物理学的生物物理.
背景情况:
- 超光谱成像对于分子识别至关重要,但与复杂的,非独立的噪音作斗争.
- 当前的无声化方法在噪声与空间相关且光谱变化时失败.
研究的目的:
- 为单个超光谱图像开发一种新的深度学习,消除架构.
- 为了应对超频谱数据中非独立噪声的挑战.
主要方法:
- 引入了自我监督的PErmutation Noise2noise Denoising (SPEND),这是一个深度学习模型.
- 采用自主监督的噪声对噪声策略,使用来自单一超频谱图像的置光谱.
- 在超光谱刺激拉曼散射和中红外光热显微镜数据上验证了Spend.
主要成果:
- 在没有地面真相数据的情况下,实现了8倍的信号噪声比改善.
- 成功消除了与空间相关的和光谱变化的噪声.
- 能够在复杂的细胞环境中准确地绘制低度生物分子的地图.
结论:
- SPEND提供了一种强大的解决方案,可以消除单一的高光谱图像,即使是复杂的噪声.
- 该方法增强了超光谱成像的能力,用于生物系统中详细的分子分析.
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