  This paper proposes an explicit way to optimize the super-resolution network
for generating visually pleasing images. The previous approaches use several
loss functions which is hard to interpret and has the implicit relationships to
improve the perceptual score. We show how to exploit the machine learning based
model which is directly trained to provide the perceptual score on generated
images. It is believed that these models can be used to optimizes the
super-resolution network which is easier to interpret. We further analyze the
characteristic of the existing loss and our proposed explicit perceptual loss
for better interpretation. The experimental results show the explicit approach
has a higher perceptual score than other approaches. Finally, we demonstrate
the relation of explicit perceptual loss and visually pleasing images using
subjective evaluation.
