Singlet lenses are free from precise assembling, aligning, and testing, which are helpful for the development of portable and low-cost microscopes. However, balancing the spectrum dispersion or chromatic aberrations using a singlet lens made of one material is difficult. Here, a novel method combining singlet lens microscopy and computational imaging, which is based on deep learning image-style-transfer algorithms, is proposed to overcome this problem in clinical pathological slide microscopy. In this manuscript, a singlet aspheric lens is used, which has a high cut-off frequency and linear signal properties. Enhanced by a trained deep learning network, it is easy to transfer the monochromatic gray-scale microscopy picture to a colorful microscopy picture, with only one single-shot recording by a monochromatic CMOS image sensor. By experiments, data analysis, and discussions, it is proved that our proposed virtual colorization microscope imaging method is effective for H&E stained tumor tissue slides in singlet microscopy. It is believable that the computational virtual colorization method for singlet microscopes would promote the low-cost and portable singlet microscopy development in medical pathological label staining observing (e.g., H&E staining, Gram staining, and fluorescent labeling) biomedical research.

中文翻译：

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深度学习虚拟着色克服单线透镜显微镜中的色差

单重透镜无需进行精确的组装，对准和测试，这对便携式和低成本显微镜的开发很有帮助。然而，使用由一种材料制成的单重透镜来平衡光谱色散或色差是困难的。在此，基于深度学习图像样式转移算法，提出了一种结合单重透镜显微镜和计算成像的新方法，以克服临床病理切片显微镜中的这一问题。在本手稿中，使用了单重非球面透镜，该透镜具有较高的截止频率和线性信号特性。通过训练有素的深度学习网络的增强，可以很容易地将单色灰度显微镜图像转换为彩色显微镜图像，而单色CMOS图像传感器仅记录一次即可。通过实验，数据分析和讨论，证明了我们提出的虚拟显色显微镜成像方法对于单线显微术中H＆E染色的肿瘤组织玻片是有效的。相信单峰显微镜的计算虚拟着色方法将促进医学病理标签染色观察（例如H＆E染色，革兰氏染色和荧光标记）生物医学研究的低成本和便携式单峰显微镜发展。