GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation

Abstract

Generating realistic electron microscopy (EM) images has been a challenging problem due to their complex global and local structures. Isola et al. proposed pix2pix, a conditional Generative Adversarial Network (GAN), for the general purpose of image-to-image translation; which fails to generate realistic EM images. We propose a new architecture for the discriminator in the GAN providing access to multiple patch sizes using skip patches and generating realistic EM images.

arXiv comment4 pages, International Conference on Computational and Mathematical Biomedical Engineering