High-throughput deep learning microscopy using multi-angle super-resolution

Biomedical applications such as pathology and hematology expect microscopes with high space-bandwidth product (SBP) which is difficult to achieve with conventional microscope setup. By applying a deep neural network, we demonstrate a high space-bandwidth product microscopic technique termed multi-angle super-resolution microscopy (MASRM) to achieve high-resolution imaging with the low-magnification objective. We design a multiple-branch deep residual network which extracts high-frequency information and color information in obliquely-illuminated low-resolution input images and generates high-resolution output. To train our network, we build a well-registered dataset in which both low-resolution input and high-resolution target are real captured images. We carry out detailed experiments to demonstrate the effectiveness of MASRM and compare it with a computational imaging technique termed Fourier ptychographic microscopy (FPM). This data-driven technique unleashes the potential of traditional microscopes with low cost and has broad prospects in biomedical applications.