Machine Learning Approaches for Solving Inverse Imaging Problems

The solution of inverse problems in imaging applications, such as, image recovery, super-resolution, and compressive sensing, to name a few, has a long history of research and development. Analytical approaches have been very successful in providing solutions to such problems. Learning approaches, and more specifically approaches based on the use of deep neural networks have recently been developed and are challenging analytical approaches in establishing the state-of-the-art. One of the first questions we will address is what are the relative advantages of analytical and learning approaches for solving inverse problems, or for a specific problem at hand which of the available approaches in our toolbox should one use? We will also discuss under what circumstances we should expect learning approaches to provide more accurate solutions than analytical approaches. We will present specific examples from our work on video super-resolution and temporal compressive sampling and draw conclusions.

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