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A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging

2019-11-06 16:50:27
Kianoush Falahkheirkhah, Kevin Yeh, Shachi Mittal, Luke Pfister, Rohit Bhargava
arXiv_CV
               
Abstract

Infrared (IR) microscopes measure spectral information that quantifies molecular content to assign the identity of biomedical cells but the spatial quality of optical microscopy to appreciate morphologic features. Here, we propose a method to utilize the semantic information of cellular identity from IR imaging with the morphologic detail of pathology images in a deep learning-based approach to image super-resolution. Using Generative Adversarial Networks (GANs), we enhance the spatial detail in IR imaging beyond the diffraction limit while retaining their spectral contrast. This technique can be rapidly integrated with modern IR microscopes to provide a framework useful for routine pathology.

Abstract (translated)

URL

https://arxiv.org/abs/1911.04410

PDF

https://arxiv.org/pdf/1911.04410.pdf

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