Histograms are used to estimate the density of data by counting the number of observations that fall into each of the bins. In our case, a two-dimensional histogram of molecular positions is created with the bin size corresponding to the pixel size of the final super-resolution image [1]. Thus, for every localized molecule, the bin value (i.e., the image brightness) at the corresponding molecular positions is incremented by one.

The histogram visualization optionally supports ‘‘jittering’’ [2]. When enabled, a random number drawn from the normal distribution, with a standard deviation equal to the computed (or user-specified) localization uncertainty, is added to the coordinates of every molecular position before creating the histogram. This step is applied multiple times and all generated histograms are averaged together. As the number of jitters increases, the final image approaches the result of the Gaussian rendering. For a small number of jitters, the histogram visualization is much faster than the Gaussian rendering but the resulting images may appear noisy.