Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance

  • Alexander Hepburn University of Bristol
  • Valero Laparra Universitat de València
  • Ryan McConville University of Bristol
  • Raul Santos-Rodriguez
Keywords: Perception, Generative Adversarial Network


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.

Author Biographies

Valero Laparra, Universitat de València

Image and Signal Processing Group

Ryan McConville, University of Bristol

Department of Engineering Mathematics

Raul Santos-Rodriguez

Department of Engineering Mathematics


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