Fast super-resolved reconstructions in fluorescence random illumination microscopy (RIM)

Random Illumination Microscopy (RIM) is a recent super-resolved fluorescence imaging technique in which the sample is recovered iteratively by matching the empirical variance of low-resolution images obtained from random speckle illuminations, with the expected variance model. RIM was shown theoretically to achieve a two-fold resolution gain and its performances have proven very robust to deteriorated imaging conditions. However, the reconstruction algorithm suffers from a slow convergence that prevents the method from being used to its full potential. Here, we show that a simple, non-iterative, linear deconvolution of the empirical standard-deviation image using an appropriate kernel can provide a super-resolved reconstruction of the sample. This first estimate can be further improved with a new accelerated iterative strategy which convergence speed is about two orders of magnitude better than that of variance matching.