GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation

arXiv:2404.00558v1 Announce Type: cross
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.

abstract adversarial arxiv cs.cv eess.iv electron gan general generate generative generative adversarial network global image image generation images image-to-image image-to-image translation microscopy network pix2pix translation type