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

• DOI: 10.48550/arxiv.1911.04410
• Publication Date: 2019-01-01
• Authors: Falahkheirkhah, K...; Yeh, Kevin; Mittal, Shachi; Pfister, Luke; Bhargava, Rohit
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Electrical Engineering and Systems Science - Image and Video Processing; Machine Learning (cs.LG)

corrected typos (the word "lack" was missing in the abstract)<br/>Infrared (IR) microscopes measure spectral information that quantifies molecular content to assign the identity of biomedical cells but lack 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.<br/>