Chinese Optics Letters, 2020, 18 (12): 121705, Published Online: Dec. 8, 2020   

Deep learning virtual colorful lens-free on-chip microscopy Download: 782次

Hua Shen 1,2,3,*Jinming Gao 1,2
Author Affiliations
1 School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China
2 MIIT Key Laboratory of Advanced Solid Laser, Nanjing University of Science and Technology, Nanjing 210094, China
3 Department of Material Science and Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA
Abstract
Currently, it is generally known that lens-free holographic microscopy, which has no imaging lens, can realize a large field-of-view imaging with a low-cost setup. However, in order to obtain colorful images, traditional lens-free holographic microscopy should utilize at least three quasi-chromatic light sources of discrete wavelengths, such as red LED, green LED, and blue LED. Here, we present a virtual colorization by deep learning methods to transfer a gray lens-free microscopy image into a colorful image. Through pairs of images, i.e., grayscale lens-free microscopy images under green LED at 550 nm illumination and colorful bright-field microscopy images, a generative adversarial network (GAN) is trained, and its effectiveness of virtual colorization is proved by applying it to hematoxylin and eosin stained pathological tissue samples imaging. Our computational virtual colorization method might strengthen the monochromatic illumination lens-free microscopy in medical pathology applications and label staining biomedical research.

Hua Shen, Jinming Gao. Deep learning virtual colorful lens-free on-chip microscopy[J]. Chinese Optics Letters, 2020, 18(12): 121705.

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