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GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation
Published 31 Mar 2024 in eess.IV and cs.CV | (2404.00558v1)
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.
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