Computational Optics and Machine Learning in Microscopic Imaging

Dear Colleagues,

Today, imaging in optical (or photonic) microscopy is based on increasingly integrated systems coupling optical, photonic, and digital approaches. Indeed, the growing needs for quantitative, specific, multiscale, and multimodal imaging imply more and more advanced digital processing on increasingly large quantities of data.

To respond to this problem, systems have become more complex, in particular through the requirement of driving light sources capable of providing temporally, spatially, and spectrally structured illumination. At the other end of the imaging chain, specific algorithms must process multidimensional and multiscale data to extract the desired information. Finally, systems are evolving toward greater agility by integrating feedback loops, allowing access to the desired information (e.g., thanks to wavefront shaping).

In this context, the very popular machine learning tools have their place and are now also integrated. They allow a better classification of the observed structures, an easier extraction of information through the large datasets available, as well as the possibility to switch from one modality to another based on the identification of the observed elements.

The field of optical and photonic microscopy thus offers today a wide variety of integrated and agile systems thanks to the implementation of digital and data processing technologies. In this Special Issue, we will therefore study these microscopic imaging systems with a special focus on the integration of these tools and the role they play in the different stages of image formation and data analysis. Integration with other imaging methods such as electron microscopy may also be considered. We will try to put forward a systemic approach illustrated by experimental realizations.

Dr. Bertrand Simon
Guest Editor

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