  In recent years there has been a growing interest in image generation through
deep learning. While an important part of the evaluation of the generated
images usually involves visual inspection, the inclusion of human perception as
a factor in the training process is often overlooked. In this paper we propose
an alternative perceptual regulariser for image-to-image translation using
conditional generative adversarial networks (cGANs). To do so automatically
(avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance
(NLPD) to measure the perceptual similarity between the generated image and the
original image. The NLPD is based on the principle of normalising the value of
coefficients with respect to a local estimate of mean energy at different
scales and has already been successfully tested in different experiments
involving human perception. We compare this regulariser with the originally
proposed L1 distance and note that when using NLPD the generated images contain
more realistic values for both local and global contrast. We found that using
NLPD as a regulariser improves image segmentation accuracy on generated images
as well as improving two no-reference image quality metrics.
