Contrast-enhanced, single-shot LED array microscopy based on Fourier ptychographic algorithm and deep learning

LED array microscopes have the advantages of miniaturisation and low cost. It has been demonstrated that LED array microscopes outperform Köhler illumination microscopes in some applications. A LED array allows for a large numerical aperture of illumination. The larger numerical aperture of illumination brings the higher spatial resolution, but the lower image contrast as well. Therefore, there is a tradeoff between resolution and contrast for LED array microscopes. The Fourier ptychographic algorithm can overcome this tradeoff by increasing image contrast without sacrificing spatial resolution. However, the Fourier ptychographic algorithm requires acquisition of multiple images, which is time-consuming and results in live sample imaging challenging. To solve this problem, we develop contrast-enhanced, single-shot LED array microscopy based on the Fourier ptychographic algorithm and deep learning. The sample to be imaged is under illumination by all LEDs of the array simultaneously. The image captured is fed to several trained convolutional neural networks to generate the same number of images that are required by the Fourier ptychographic algorithm. We experimentally present that the image contrast of the final reconstruction is remarkably improved in comparison with the image captured. The proposed method can also produce chromatic-aberration-free results, even when an objective without aberration correction is used. We believe the method might provide live sample imaging with a low-cost approach.