用普通相机+深度学习，一秒拍出多光谱图像。
Snapshot multi-spectral imaging through defocusing and a Fourier imager network
- 利用镜头色差导致的模糊差异编码光谱信息
- 六波段测试准确率达92.98%，重建图像质量高
- 无需特殊滤镜，适合医疗、农业等场景
多光谱成像可同时获取场景的空间与光谱信息，广泛应用于遥感、生物医学和农业监测等领域。本文提出一种快照式多光谱成像方法，仅使用标准单色传感器，无需额外光谱滤镜或定制元件。系统利用波长依赖性离焦引起的固有色差作为天然物理编码手段，将多光谱信息嵌入图像中；再通过基于深度学习的多光谱傅里叶成像网络（mFIN）快速解码。实验在六个照明波段上验证，输入通道预测整体准确率达92.98%，并在多种测试物体上实现了稳健的多光谱图像重建。该深度学习框架结合快照采集与单色传感器，可实现高质量多光谱图像重建，适用于生物医学、工业质检及农业等应用。
原文摘要 · Abstract (English)
Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. Here, we introduce a snapshot multi-spectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multi-spectral information; this encoded image information is rapidly decoded via a deep learning-based multi-spectral Fourier Imager Network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 92.98% for predicting the illumination channels at the input and achieved a robust multi-spectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multi-spectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others.
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