Deep learning virtual colorization overcoming chromatic aberrations in singlet lens microscopy
Yinxu Bian,
Yannan Jiang,
Yuran Huang,
Xiaofei Yang,
Weijie Deng,
Hua Shen,
Renbing Shen,
Cuifang Kuang
Affiliations
Yinxu Bian
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Yannan Jiang
Department of General Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou 215002, China
Yuran Huang
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
Xiaofei Yang
School of Optoelectronic Science and Engineering & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou 215006, China
Weijie Deng
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Hua Shen
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Renbing Shen
Department of General Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou 215002, China
Cuifang Kuang
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
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.
