• Corpus ID: 204852297

Interactive Image Restoration

@article{Han2019InteractiveIR,
  title={Interactive Image Restoration},
  author={Zhiwei Han and Thomas Weber and Stefan Matthes and Yuanting Liu and Hao Shen},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.11059}
}
Machine learning and many of its applications are considered hard to approach due to their complexity and lack of transparency. One mission of human-centric machine learning is to improve algorithm transparency and user satisfaction while ensuring an acceptable task accuracy. In this work, we present an interactive image restoration framework, which exploits both image prior and human painting knowledge in an iterative manner such that they can boost on each other. Additionally, in this system… 

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