Abstract Compared with monochromatic or RGB imaging technologies, multi-spectral imaging technology is beneficial for obtaining more information details from images of different wavelengths with better contrast. A novel portable and cost-effective multispectral microscope is proposed, whose highlights are firstly imaging by a special designed singlet lens with only controlled rotational symmetric aberrations and then removing the controlled aberrations by deep learning computational imaging method to improve the resolution. Our method helps to reduce the extreme difficulties of singlet lens fabrication due to its simple surface produced by the unnecessarily supreme aberration optimization, while ensuring the resolution. In this manuscript, we introduce singlet lens design methods to connect Zernike polynomial coefficients with singlet lens parameters through wavefront aberrations. Then, the deep learning networks, parameters and working environments are provided to computationally enhance the resolution. By imaging a gold standard resolution pattern and typical bio-samples, experiment results demonstrate that our method and demo-setups achieve multi-spectral microscopy well and cost-effectively. It is believable that our portable deep learning singlet multi-spectral microscope would be an alternate to the conventional approaches and applied in biology, chemistry, environmental science, especially in resource-limited areas.

中文翻译：

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便携式深度学习单线态多光谱显微镜

摘要 与单色或RGB成像技术相比，多光谱成像技术有利于从不同波长、对比度更好的图像中获取更多的信息细节。提出了一种新型的便携式和高性价比的多光谱显微镜，其亮点是首先通过特殊设计的单透镜成像，仅控制旋转对称像差，然后通过深度学习计算成像方法去除受控像差以提高分辨率。我们的方法有助于减少单透镜制造的极端困难，因为其表面简单，由不必要的最高像差优化产生，同时确保分辨率。在这份手稿中，我们介绍了单透镜设计方法，通过波前像差将泽尼克多项式系数与单透镜参数联系起来。然后，提供深度学习网络、参数和工作环境以在计算上提高分辨率。通过对黄金标准分辨率模式和典型生物样本进行成像，实验结果表明我们的方法和演示设置可以很好地且经济高效地实现多光谱显微镜。相信我们的便携式深度学习单线态多光谱显微镜将替代传统方法并应用于生物学、化学、环境科学，尤其是在资源有限的地区。通过对黄金标准分辨率模式和典型生物样本进行成像，实验结果表明我们的方法和演示设置可以很好地且经济高效地实现多光谱显微镜。相信我们的便携式深度学习单线态多光谱显微镜将替代传统方法并应用于生物学、化学、环境科学，尤其是在资源有限的地区。通过对黄金标准分辨率模式和典型生物样本进行成像，实验结果表明我们的方法和演示设置可以很好地且经济高效地实现多光谱显微镜。相信我们的便携式深度学习单线态多光谱显微镜将替代传统方法并应用于生物学、化学、环境科学，尤其是在资源有限的地区。