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Microscopy images are powerful tools and widely used in the majority ofresearch areas, such as biology, chemistry, physics and materials fields byvarious microscopies (scanning electron microscope (SEM), atomic forcemicroscope (AFM) and the optical microscope, et al.). However, most of themicroscopy images are colorless due to the unique imaging mechanism. Thoughinvestigating on some popular solutions proposed recently about colorizingimages, we notice the process of those methods are usually tedious,complicated, and time-consuming. In this paper, inspired by the achievement ofmachine learning algorithms on different science fields, we introduce twoartificial neural networks for gray microscopy image colorization: Anend-to-end convolutional neural network (CNN) with a pre-trained model forfeature extraction and a pixel-to-pixel neural style transfer convolutionalneural network (NST-CNN), which can colorize gray microscopy images withsemantic information learned from a user-provided colorful image at inferencetime. The results demonstrate that our algorithm not only can colorize themicroscopy images under complex circumstances precisely but also make the colornaturally according to the training of a massive number of nature images withproper hue and saturation.

Nanoscale Microscopy Images Colorization Using Neural Networks