Image Super-Resolution (ISR)

The goal of this project is to upscale and improve the quality of low resolution images.

This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components.

The implemented networks include:

• The super-scaling Residual Dense Network described in Residual Dense Network for Image Super-Resolution (Zhang et al. 2018)
• The super-scaling Residual in Residual Dense Network described in ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (Wang et al. 2018)
• A multi-output version of the Keras VGG19 network for deep features extraction used in the perceptual loss
• A custom discriminator network based on the one described in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGANS, Ledig et al. 2017)

Read the full documentation at: https://idealo.github.io/image-super-resolution/.

Citation

Please cite our work in your publications if it helps your research.

@misc{cardinale2018isr,
  title={ISR},
  author={Francesco Cardinale et al.},
  year={2018},
  howpublished={\url{https://github.com/idealo/image-super-resolution}},
}

Maintainers

• Francesco Cardinale, github: cfrancesco
• Dat Tran, github: datitran