Light microscopy (LM) approaches are commonly used to attain a description of the cell structure. Even though LM, if compared to electron microscopy (EM), represents a very fast approach, its resolution is, in principle, much lower than in the case of EM. To improve the LM resolution, computational methods based on removal of the image blur are frequently implemented in cell biology studies. One of the standard deblurring approaches is image restoration through deconvolution algorithms. Even though this method of mathematical remodeling of microscopically observed objects represents an efficient tool of current cell biology, it is legitimate to ask what the limits of its use are. We demonstrate that, in the specific case of the fluorescence mapping of active ribosomal genes in HeLa cell nucleoli, restoration generates a biased result. On restoration of model images, we demonstrate the difficulties of one of the most effective deconvolution algorithms during the restoration of ring-shaped fluorescent objects of a diameter comparable to the microscope resolution limit. In the case involving the mapping of nucleolar transcription in HeLa cells, not the restored fluorescence images, but rather the EM images show the true distribution of active ribosomal genes.