  Sub-diffraction resolution, gentle sample illumination, and the possibility
to image in multiple colors make Structured Illumination Microscopy (SIM) an
imaging technique which is particularly well suited for live cell observations.
Here, we present Machine learning Assisted Interferometric-SIM (MAI-SIM), an
easy-to-implement method for high speed SIM imaging in multiple colors. The
instrument is based on an interferometer design in which illumination patterns
are generated, rotated, and stepped in phase through movement of a single
galvanometric mirror element. The design is robust, flexible, and the pattern
generation process works for all wavelengths. We complement the unique
properties of interferometric SIM with a machine learning toolbox that is
simple and efficient to use and is superior to existing methods for the
reconstruction of super-resolved images recorded by the instrument. The
framework permits real-time SIM reconstructions to be performed in multiple
colors, providing the user with instant visualization of the super-resolved
images. We demonstrate the capability of MAI-SIM on live biological samples and
capture super-resolution images in multiple colors simultaneously over large
fields of view. Finally, we embrace a fully open design philosophy to bring the
advantages of MAI-SIM to as many users as possible and provide full details on
system design and software.
