Deep learning virtual Zernike phase contrast imaging for singlet microscopy

Singlet microscopy is very attractive for the development of cost-effective and portable microscopes. In contrast to conventional microscope objectives, which consist multiple lenses, manufacturing process singlet lenses done without extensive assembling aligning. this manuscript, we report a novel virtual Zernike phase setup unstained pathological tumor tissue slides. setup, objective consists only one lens. There no need inset plate, even more expensive than whole brightfield setup. The virtually achieved by deep learning computational imaging method. For practical time less 100 ms, far that other quantitative algorithms. With conceptual demo experimental proved our proposed method be competitive with research-level effective transparent It believed potential low-cost microscopes benefits resource-limited areas.