  Since the advent of deep convolutional neural networks (DNNs), computer
vision has seen an extremely rapid progress that has led to huge advances in
medical imaging. This article does not aim to cover all aspects of the field
but focuses on a particular topic, image-to-image translation. Although the
topic may not sound familiar, it turns out that many seemingly irrelevant
applications can be understood as instances of image-to-image translation. Such
applications include (1) noise reduction, (2) super-resolution, (3) image
synthesis, and (4) reconstruction. The same underlying principles and
algorithms work for various tasks. Our aim is to introduce some of the key
ideas on this topic from a uniform point of view. We introduce core ideas and
jargon that are specific to image processing by use of DNNs. Having an
intuitive grasp of the core ideas of and a knowledge of technical terms would
be of great help to the reader for understanding the existing and future
applications. Most of the recent applications which build on image-to-image
translation are based on one of two fundamental architectures, called pix2pix
and CycleGAN, depending on whether the available training data are paired or
unpaired. We provide computer codes which implement these two architectures
with various enhancements. Our codes are available online with use of the very
permissive MIT license. We provide a hands-on tutorial for training a model for
denoising based on our codes. We hope that this article, together with the
codes, will provide both an overview and the details of the key algorithms, and
that it will serve as a basis for the development of new applications.
