PyTorch version of the paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
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Updated
Jun 2, 2017 - Python
PyTorch version of the paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
A modern PyTorch implementation of SRGAN
PyTorch implementation of the paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
A PyTorch implementation of SRGAN based on the paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Generative Adversarial Network for single image super-resolution in high content screening microscopy images
An implement of SRGAN(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) for tensorflow version
Image Super-Resolution Using SRCNN, DRRN, SRGAN, CGAN in Pytorch
Pixel x4 is a image super-resolution deep learning algorithm. It uses both the deep convolutional GANs for generating realistic images and the distance based loss function for creating visually similar images.
SRGAN (super resolution generative adversarial networks) with WGAN loss function in TensorFlow
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".
collection of super-resolution models & algorithms
UGP 1 for 5th semester
Object-Oriented Image Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
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