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Removing reflection artefacts from a single image is a problem of boththeoretical and practical interest, which still presents challenges because ofthe massively ill-posed nature of the problem. In this work, we propose atechnique based on a novel optimisation problem. Firstly, we introduce a simpleuser interaction scheme, which helps minimise information loss inreflection-free regions. Secondly, we introduce an $H^2$ fidelity term, whichpreserves fine detail while enforcing global colour similarity. We show thatthis combination allows us to mitigate some major drawbacks of the existingmethods for reflection removal. We demonstrate, through numerical and visualexperiments, that our method is able to outperform the state-of-the-art methodsand compete with recent deep-learning approaches.

Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal