用自监督方法从单张高光谱图像中去除相关噪声，提升分子成像精度。
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging
- 通过奇偶波段置换生成噪声对，实现无需真值的自监督去噪训练。
- 在无真实数据情况下实现信噪比8倍提升，成功检测低浓度生物分子。
- 适用于复杂细胞环境中的指纹区与沉默区分子成像，适合生物医学研究者。
高光谱成像广泛用于目标分子的光谱与空间识别，但常受复杂噪声污染。现有去噪方法多依赖独立同分布噪声假设，在处理非独立噪声时表现不佳。本文提出自监督排列噪声到噪声去噪（SPEND）架构，专为从单张高光谱图像堆栈中去除非独立噪声而设计。以高光谱受激发射拉曼散射和中红外光热显微镜为测试平台，其中噪声具有空间相关性和光谱差异性。基于单张高光谱图像，SPEND通过奇偶波段置换生成两组噪声特性相同的图像堆栈，并利用成对数据进行高效的自监督噪声到噪声训练。实验表明，SPEND在无真实数据条件下实现了8倍信噪比提升，成功实现低浓度生物分子在指纹区与沉默区的精确定位，证明其在复杂细胞环境中的鲁棒性。
原文摘要 · Abstract (English)
Hyperspectral imaging has been widely used for spectral and spatial identification of target molecules, yet often contaminated by sophisticated noise. Current denoising methods generally rely on independent and identically distributed noise statistics, showing corrupted performance for non-independent noise removal. Here, we demonstrate Self-supervised PErmutation Noise2noise Denoising (SPEND), a deep learning denoising architecture tailor-made for removing non-independent noise from a single hyperspectral image stack. We utilize hyperspectral stimulated Raman scattering and mid-infrared photothermal microscopy as the testbeds, where the noise is spatially correlated and spectrally varied. Based on single hyperspectral images, SPEND permutates odd and even spectral frames to generate two stacks with identical noise properties, and uses the pairs for efficient self-supervised noise-to-noise training. SPEND achieved an 8-fold signal-to-noise improvement without having access to the ground truth data. SPEND enabled accurate mapping of low concentration biomolecules in both fingerprint and silent regions, demonstrating its robustness in sophisticated cellular environments.
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