Deep learning for image restoration

The activity involves the development of deep learning architectures for several image restoration problems, including image denoising, single-image and multi-image super-resolution, image deblurring/deconvolution, and reconstruction from projections. We have employed several types of deep neural networks for these problems, including CNNs, graph neural networks, generative adversarial networks, obtaining state-of-the-art results via the custom design of highly efficient architectures based on permutation invariance.

Application areas are in the field of satellite imaging and computational imaging for smartphones and cameras.


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ERC Sector:

  • PE7_7 Signal processing

Keywords:

  • Artificial neural networks
  • Image processing
  • Deep learning
  • Image restoration

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