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What do regularisers do?

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VMVW02 - Generative models, parameter learning and sparsity

Which regulariser is the best? Is any of them any good? Do they introduce artefacts? What other qualitative properties do they have? These are some questions, on which I want to shed some light of the early dawn. Specifically, I will firstly discuss recent work on natural conditions, based on an analytical study of a bilevel learning approach, to ensure that regularisation does indeed improve an image. Based on a more computational study, based on bilevel learning, I will also try to answer the question, which constructed regulariser is the best one. Secondly, I will discuss geometrical aspects of the solutions to higher-order regularised imaging problems.

This talk is part of the Isaac Newton Institute Seminar Series series.

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