Abstract
Reflections are a common artifact in images taken
through glass windows. Automatically removing the re-
flection artifacts after the picture is taken is an ill-posed
problem. Attempts to solve this problem using optimization
schemes therefore rely on various prior assumptions from
the physical world. Instead of removing reflections from
a single image, which has met with limited success so far,
we propose a novel approach to suppress reflections. It is
based on a Laplacian data fidelity term and an l0 gradient
sparsity term imposed on the output. With experiments on
artificial and real-world images we show that our reflection
suppression method performs better than the state-of-theart reflection removal techniques.