GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation
<|reference_start|>GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation: 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.<|reference_end|>
@article{roy2024gan,
    title={GAN with Skip Patch Discriminator for Biological Electron Microscopy
  Image Generation},
    author={Nishith Ranjon Roy, Nailah Rawnaq, Tulin Kaman},
    journal={arXiv preprint arXiv:2404.00558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00558},
    primaryClass={eess.IV cs.CV}
}