Results of the joint deconvolution algorithm fusing both classical and quantum information for 2 photon absorption. Insets (A) and (D) are the highest resolution images using classical information (k = 1, q = 4) for a mean photon count of = 500 (A) and = 5 million (D). Insets (B) and (E) combine both classical and quantum image orders (k = 1,2,3) also for mean photon counts of = 500 (B) and = 5 million(E). Inset (C) and (F) show the radial average ofthe Fourier transform of the reconstructions compared to the actual image used in the simulation for mean photon count levels = 500 (C) and = 5 million (F). Scale bar is 5λ.

A revolution is under way in optical microscopy where the quantum properties of light are exploited to extract additional information from quantum correlations that are absent in the classical interpretation. Such quantum information brings new possibilities but also its own set of limitations. Here, we develop a broader computational imaging approach to fuse quantum and classical information to provide a general solution that jointly exploits both forms of information for super-resolution microscopy.

Over the past few decades, numerous super-resolution microscopy methods have emerged that circumvent the optical diffraction barrier by bringing new information into the measurements. This new content is injected by exploiting photophysical properties such as structured illumination, localization, saturated absorption, or coherent nonlinear scattering. These super-resolution imaging techniques treat the total light signal collected during the image exposure time classically, and thus, information content is consequently restricted.