  It is well-known that a number of excellent super-resolution (SR) methods
using convolutional neural networks (CNNs) generate checkerboard artifacts. A
condition to avoid the checkerboard artifacts is proposed in this paper. So
far, checkerboard artifacts have been mainly studied for linear multirate
systems, but the condition to avoid checkerboard artifacts can not be applied
to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for
CNNs, and apply the proposed structure to some typical SR methods to confirm
the effectiveness of the new scheme. Experiment results demonstrate that the
proposed structure can perfectly avoid to generate checkerboard artifacts under
two loss conditions: mean square error and perceptual loss, while keeping
excellent properties that the SR methods have.
