  We demonstrate that a deep neural network can significantly improve optical
microscopy, enhancing its spatial resolution over a large field-of-view and
depth-of-field. After its training, the only input to this network is an image
acquired using a regular optical microscope, without any changes to its design.
We blindly tested this deep learning approach using various tissue samples that
are imaged with low-resolution and wide-field systems, where the network
rapidly outputs an image with remarkably better resolution, matching the
performance of higher numerical aperture lenses, also significantly surpassing
their limited field-of-view and depth-of-field. These results are
transformative for various fields that use microscopy tools, including e.g.,
life sciences, where optical microscopy is considered as one of the most widely
used and deployed techniques. Beyond such applications, our presented approach
is broadly applicable to other imaging modalities, also spanning different
parts of the electromagnetic spectrum, and can be used to design computational
imagers that get better and better as they continue to image specimen and
establish new transformations among different modes of imaging.
