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Super-resolution microscopy techniques have pushed-down the limits of resolution in optical imaging by more than an order of magnitude. However, these methods often require long acquisition times in addition to complex setups and sample preparation protocols. Super-resolution optical fluctuation imaging (SOFI) emerged over ten years ago as an approach that exploits temporal and spatial correlations within the acquired images to obtain increased resolution with less strict requirements. This review follows the progress of SOFI from its first demonstration to the development of a branch of methods that treat fluctuations as a source of contrast, rather than noise. Among others, we highlight the implementation of SOFI with standard fluorescent proteins as well as microscope modifications that facilitate 3D imaging and the application of modern cameras. Going beyond the classical framework of SOFI, we explore different innovative concepts from deep neural networks all the way to a quantum analogue of SOFI, antibunching microscopy. While SOFI has not reached the same level of ubiquity as other super-resolution methods, our overview finds significant progress and substantial potential for the concept of leveraging fluorescence fluctuations to obtain super-resolved images.

Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods