The interface of deep learning and imaging has seen extraordinary progress in the past few years as computational power now enables image processing that can exceed human capability. Much of the recent work at this interface involves the application of variants of convolutional neural networks, for a wide variety of techniques including image enhancement, style transfer and labelling. However, whilst deep learning can unlock extremely powerful capabilities, the collection and processing of appropriate training data remains a significant challenge. In this talk, a brief tutorial on the practical application of neural networks for image processing will be presented, followed by experimental results associated with optical and scanning electron microscopy. The focus of this talk will be on the demonstration of image enhancement of optical microscopy from 20x resolution to 1500x, whilst simultaneously identifying the objects present and hence enabling automated labelling, colour-enhancing and removal of specific objects in the magnified image.
We demonstrate the application of deep learning for the identification of particles, directly from their backscattered light. The particles were illuminated using a single-mode fibre-coupled laser light source and the scattered light was collected by a 30-core optical fibre. The technique enabled identification of the specific species of pollen grains with an accuracy of ~97%, even in the presence of high levels of background light equivalent to daytime sunlight. In addition, the technique determined the distance between the fibre tip and the particles with an accuracy of ± 6 µm.
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