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Abstract: Deep learning has significantly advanced several areas of imaging, from image analysis tasks such as classification and segmentation to image restoration tasks. Besides being able to produce photorealistic images, generative architectures such as GANs and diffusion models have turned out to be very useful for solving inverse problems, providing powerful priors that can be used even with lack of ground truth data, or lack of training data at all. This talk will focus on recent advances in deep models for restoration, touching on aspects such as perception, complexity and priors. I will cover models for supervised, self-supervised, unsupervised and one-shot restoration, in the single- and multi-image case, as well as the generation of multiple “good” solutions that are consistent with the observed data. I will show examples of models learning non-local relationships in the data, along with applications to a variety of data types including optical/radar satellite images and point clouds. I will discuss the implications of these methods on accuracy, perceptual quality and complexity, and I will discuss multimodality as a way to further improve image restoration accuracy.

Bio:  Enrico Magli is a Full Professor with Politecnico di Torino, Italy, where he leads the Image Processing and Learning group. He performs research in the fields of deep learning for image and video processing and image compression, with applications to vision and remote sensing. He is a Senior Associate Editor of IEEE Journal on Selected Topics in Signal Processing, and a former Associate Editor of IEEE T-MM and IEEE T-CSVT. He chaired the IEEE Technical Committee on Multimedia Signal processing. He is a Fellow of the IEEE, a Fellow of the ELLIS Society for the advancement of artificial intelligence in Europe, and has been an IEEE Distinguished Lecturer. He was a co-recipient of the IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award, the IEEE ICIP 2015 Best Student Paper Award (as senior author), the IEEE ICIP 2019 Best Paper Award, the IEEE Multimedia 2019 Best Paper Award. He has received an ERC consolidator grant in 2011 and an ERC proof-of-concept grant in 2015.

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