DeepRED: Deep Image Prior Powered by RED

Understand

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years.

• Recently, this field has been immensely influenced by the emergence of deep-learning techniques.
• One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018).
• DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture.