A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging
This provides a framework for enhancing IR imaging in routine pathology, addressing a domain-specific bottleneck in biomedical imaging.
The authors tackled the problem of limited spatial resolution in infrared (IR) microscopes by developing a deep learning method using Generative Adversarial Networks (GANs) to enhance spatial detail beyond the diffraction limit while preserving spectral contrast, enabling improved morphologic feature appreciation for biomedical cell analysis.
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.