  Most distortion correction methods focus on simple forms of distortion, such
as radial or linear distortions. These works undistort images either based on
measurements in the presence of a calibration grid, or use multiple views to
find point correspondences and predict distortion parameters. When possible
distortions are more complex, e.g. in the case of a camera being placed behind
a refractive surface such as glass, the standard method is to use a calibration
grid. Considering a high variety of distortions, it is nonviable to conduct
these measurements. In this work, we present a single view distortion
correction method which is capable of undistorting images containing
arbitrarily complex distortions by exploiting recent advancements in
differentiable image sampling and in the usage of semantic information to
augment various tasks. The results of this work show that our model is able to
estimate and correct highly complex distortions, and that incorporating
semantic information mitigates the process of image undistortion.
