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.
- PE7_7 Signal processing
- Artificial neural networks
- Image processing
- Deep learning
- Image restoration