Rating Super-Resolution Microscopy Images With Deep Learning

In order to improve their understanding, cellular mechanisms to the imaging process, or the observability of specific structures. Superresolution we consider a network made of 6 convolutional layers microscopes are highly specialized devices, significantly and 2 fully connected layers. An ELU activation (Exponential more complex to use than conventional optical microscopes, Linear Unit) is used after each convolutional and fully hence reducing their accessibility. Max pooling (kernel 2x2, stride 1) is added overall quality of the obtained images can vary a lot depending after each convolutional unit. Batch normalization is applied on the imaging parameters or the biological structure of to all the layers except the first one.