Corpus ID: 189762113

Training Image Estimators without Image Ground-Truth

@inproceedings{Xia2019TrainingIE,
  title={Training Image Estimators without Image Ground-Truth},
  author={Zhihao Xia and Ayan Chakrabarti},
  booktitle={NeurIPS},
  year={2019}
}

• Zhihao Xia, Ayan Chakrabarti
• Published in NeurIPS 2019
• Computer Science

Deep neural networks have been very successful in image estimation applications such as compressive-sensing and image restoration, as a means to estimate images from partial, blurry, or otherwise degraded measurements. [...] Key Method We demonstrate that our framework can be applied for both regular and blind image estimation tasks, where in the latter case parameters of the measurement model (e.g., the blur kernel) are unknown: during inference, and potentially, also during training. We evaluate our method…Expand

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