  Applications that involve supervised training require paired images.
Researchers of single image super-resolution (SISR) create such images by
artificially generating blurry input images from the corresponding ground
truth. Similarly we can create paired images with the canny edge. We propose
Generator From Edges (GFE) [Figure 2]. Our aim is to determine the best
architecture for GFE, along with reviews of perceptual loss [1, 2]. To this
end, we conducted three experiments. First, we explored the effects of the
adversarial loss often used in SISR. In particular, we uncovered that it is not
an essential component to form a perceptual loss. Eliminating adversarial loss
will lead to a more effective architecture from the perspective of hardware
resource. It also means that considerations for the problems pertaining to
generative adversarial network (GAN) [3], such as mode collapse, are not
necessary. Second, we reexamined VGG loss and found that the mid-layers yield
the best results. By extracting the full potential of VGG loss, the overall
performance of perceptual loss improves significantly. Third, based on the
findings of the first two experiments, we reevaluated the dense network to
construct GFE. Using GFE as an intermediate process, reconstructing a facial
image from a pencil sketch can become an easy task.
