Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
Yuanlong Zhang, Xiaofei Song, Jiachen Xie, Jing Hu, Jiawei Chen, Xiang Li, Haiyu Zhang, Qiqun Zhou, Lekang Yuan, Chui Kong, Yibing Shen, Jiamin Wu, Lu Fang, Qionghai Dai
Abstract
Abstract The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm 3 and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.