Improving correlation based super-resolution microscopy images through image fusion by self-supervised deep learning.

Super-resolution imaging is a powerful tool in modern biological research, allowing for the optical observation of subcellular structures with great detail. In this paper, we present a deep learning approach for image fusion of intensity and super-resolution optical fluctuation imaging (SOFI) microscopy images. We construct a network that can successfully combine the advantages of these two imaging methods, producing a fused image with a resolution comparable to that of SOFI and an SNR comparable to that of the intensity image.