Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

@article{Ledig2017PhotoRealisticSI,
  title={Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network},
  author={Christian Ledig and Lucas Theis and Ferenc Huszar and Jose Caballero and Andrew P. Aitken and Alykhan Tejani and Johannes Totz and Zehan Wang and Wenzhe Shi},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={105-114}
}
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting… CONTINUE READING
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