Mehran Ebrahimi, from the Faculty of Science of the Ontario Tech University, Canada
Date & time: Wednesday, November 20th, 15:30h
Location: Instituto Superior Técnico, Lisbon, Room 4.12/LT2 (4th floor of the North Tower)
In many practical problems in the field of applied sciences, the features of most interest cannot be observed directly, but have to be inferred from other, observable quantities. The problem of solving an unknown object from the observed quantities is called an inverse problem. Many classical problems in imaging can be modeled as inverse problems. Many real-world inverse problems are ill-posed, mainly due to the lack of existence of a unique solution. The procedure of providing an acceptable unique solution to such problems is known as regularization. Indeed, much of the progress in image processing in the past few decades has been due to advances in the formulation and practice of regularization. This, coupled with the progress in the areas of optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems.
In this talk, we will revisit a number of inverse problems including image registration (alignment), image inpainting (completion), super-resolution (resolution enhancement), and present some recent research ideas mainly aimed at medical imaging applications. Furthermore, we present an approach based on deep convolutional neural networks to address two image restoration problems, image inpainting, and super-resolution. The method applies our so-called “Edge-Connect”, a two-stage adversarial model that contains an edge generator followed by an image completion network. We evaluate the model and observe that it outperforms current state-of-the-art techniques quantitatively and qualitatively.
Mehran Ebrahimi is an Associate Professor with the Faculty of Science, University of Ontario Institute of Technology (Ontario Tech), Canada. He received his Ph.D. from the Department of Applied Mathematics, University of Waterloo, in 2008, and he currently leads the Imaging Lab in the Faculty of Science at Ontario Tech. His research is focused on the diverse area of machine learning, mathematical imaging, and inverse problems. His long-term research objective is directed towards developing and validating efficient numerical methodologies for solving real-world ill-posed inverse problems in the field of medical imaging.