Overview of the proposed deep-learning-based image superresolution.

We use a deep neural network for superresolution that takes a low-resolution image as input and yields a high-resolution image as output, which in our case represent coarse and fine bathymetric charts, respectively. First, in the training phase, we let the network learn how to estimate the high-resolution image from the low-resolution one, using a dataset consisting of many pairs of low- and high-resolution images. This is done by minimizing a loss function, which is defined as the difference between the estimated high-resolution image and the true high-resolution image corresponding to the low-resolution one. Then, in the testing phase, we can let the network predict a desired, unknown high-resolution image from each newly-given low resolution image.