Phase-contrast computed tomography enables the visualization of weakly-absorbing samples with high contrast. Speckle-based imaging (SBI) is a phase-sensitive X-ray imaging technique that requires the use of a wavefront marker (typically a sandpaper) to retrieve multi-modal information: absorption, refraction and scattering. These quantities are derived by analyzing the distortions in a reference pattern generated when the sample is inserted into the beam. The Unified Modulated Pattern Analysis (UMPA) model is a speckle-tracking method capable of processing such datasets. While high-resolution tomographic reconstructions can be achieved at the synchrotron, there is usually a trade-off with sample dimensions. Here, we use UMPA with a multi-frame approach for signal retrieval, enabling the expansion of the reconstructed field-of-view (FOV) by moving the sample instead of the modulator transversely to the beam. We demonstrate this technique on a human placental tissue sample.
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.
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