Super resolution in microscopic images of SARS-CoV-2 through deep learning

In this work, we carried out a study on the importance of super-resolution in SARS-CoV-2 microscopic images. We analysed the impossibility of realising super-resolution in SARS-CoV-2 microscopic images, through deep learning, without a database of real images that allows training of convolutional neural networks. In this sense, we proposed an intelligent strategy that made it possible to select, by means of deep learning, the most appropriate algorithm from several previously developed ones. In other words, the strategy consisted in analysing, via deep learning, the characteristics of the microscopic images, classifying them and recommending the most appropriate algorithm to carry out the super-resolution task. In order to evaluate the effectiveness of the obtained results, we realised a quantitative comparison of the selected algorithm through our strategy with the one proposed by experts in computer vision. The efficiency of our smart strategy was over 97%.